Interpreting polygenic score effects in sibling analysis.

Researchers often claim that sibling analysis can be used to separate causal genetic effects from the assortment of biases that contaminate most downstream genetic studies (e.g. polygenic score predictors). Indeed, typical results from sibling analysis show large (>50%) attenuations in the associations between polygenic scores and phenotypes compared to non-sibling analysis, consistent with researchers' expectations about bias reduction. This paper explores these expectations by using family (quad) data and simulations that include indirect genetic effect processes and evaluates the ability of sibling analysis to uncover direct genetic effects of polygenic scores. We find that sibling analysis, in general, fail to uncover direct genetic effects; indeed, these models have both upward and downward biases that are difficult to sign in typical data. When genetic nurture effects exist, sibling analysis creates "measurement error" that attenuates associations between polygenic scores and phenotypes. As the correlation between direct and indirect effect changes, this bias can increase or decrease. Our findings suggest that interpreting results from sibling analysis aimed at uncovering direct genetic effects should be treated with caution.

Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease.

Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and non-random participation of parental illness survey, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that current AD GWAX provide highly misleading genetic correlations between AD risk and higher education which subsequently affects a variety of genetic epidemiologic applications involving AD and cognition. Our study sheds important light on the design and analysis of mid-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

The production of within-family inequality: Insights and implications of integrating genetic data.

The integration of genetic data within large-scale social and health surveys provides new opportunities to test long-standing theories of parental investments in children and within-family inequality. Genetic predictors, called polygenic scores, allow novel assessments of young children's abilities that are uncontaminated by parental investments, and family-based samples allow indirect tests of whether children's abilities are reinforced or compensated. We use over 16,000 sibling pairs from the UK Biobank to test whether the relative ranking of siblings' polygenic scores for educational attainment is consequential for actual attainments. We find evidence consistent with compensatory processes, on average, where the association between genotype and phenotype of educational attainment is reduced by over 20% for the higher-ranked sibling compared to the lower-ranked sibling. These effects are most pronounced in high socioeconomic status areas. We find no evidence that similar processes hold in the case of height or for relatives who are not full biological siblings (e.g. cousins). Our results provide a new use of polygenic scores to understand processes that generate within-family inequalities and also suggest important caveats to causal interpretations the effects of polygenic scores using sibling difference designs. Future work should seek to replicate these findings in other data and contexts.

Mid-high-frequency error suppression of small optical aspheric molds.

To suppress the mid-high-frequency error of small optical tungsten carbide aspheric molds, it is proposed to quickly select the critical process parameters by simulating the residual error after convolution of the tool influence function (TIF). After polishing for 10.47 min by the TIF, two simulation optimizations, RMS and Ra, converge to 9.3 and 5.347 nm, respectively. Their convergence rates are improved by 40% and 7.9%, respectively, compared to ordinary TIF. Then, a faster and more high-quality multi-tool combination smoothing suppression method is proposed, and the corresponding polishing tools are designed. Finally, the global Ra of the aspheric surface converges from 5.9 to 4.5 nm after smoothing for 5.5 min with a disc-shaped polishing tool with a fine microstructure and maintains an excellent low-frequency error (PV 0.0781 µm).

Decomposing heritability and genetic covariance by direct and indirect effect paths.

Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.

A quantile integral linear model to quantify genetic effects on phenotypic variability.

Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

GWAS on birth year infant mortality rates provides evidence of recent natural selection.

Following more than a century of phenotypic measurement of natural selection processes, much recent work explores relationships between molecular genetic measurements and realized fitness in the next generation. We take an innovative approach to the study of contemporary selective pressure by examining which genetic variants are "sustained" in populations as mortality exposure increases. Specifically, we deploy a so-called "regional GWAS" (genome-wide association study) that links the infant mortality rate (IMR) by place and year in the United Kingdom with common genetic variants among birth cohorts in the UK Biobank. These cohorts (born between 1936 and 1970) saw a decline in IMR from above 65 to under 20 deaths per 1,000 live births, with substantial subnational variations and spikes alongside wartime exposures. Our results show several genome-wide significant loci, including LCT and TLR10/1/6, related to area-level cohort IMR exposure during gestation and infancy. Genetic correlations are found across multiple domains, including fertility, cognition, health behaviors, and health outcomes, suggesting an important role for cohort selection in modern populations.

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

What's the use?

A theoretical framework predicts that using polygenic screening to select embryos against traits that depend on many genes has few benefits.

PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics.

Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis.

SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits.

Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.

Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies.

Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic, and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower body mass index, less-active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. A polygenic transmission disequilibrium test showed a significant overtransmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder.

Recent advances in consortium-scale genome-wide association studies (GWAS) have highlighted the involvement of common genetic variants in autism spectrum disorder (ASD), but our understanding of their etiologic roles, especially the interplay with rare variants, is incomplete. In this work, we introduce an analytical framework to quantify the transmission disequilibrium of genetically regulated gene expression from parents to offspring. We applied this framework to conduct a transcriptome-wide association study (TWAS) on 7,805 ASD proband-parent trios, and replicated our findings using 35,740 independent samples. We identified 31 associations at the transcriptome-wide significance level. In particular, we identified POU3F2 (p = 2.1E-7), a transcription factor mainly expressed in developmental brain. Gene targets regulated by POU3F2 showed a 2.7-fold enrichment for known ASD genes (p = 2.0E-5) and a 2.7-fold enrichment for loss-of-function de novo mutations in ASD probands (p = 7.1E-5). These results provide a novel connection between rare and common variants, whereby ASD genes affected by very rare mutations are regulated by an unlinked transcription factor affected by common genetic variations.

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations.

The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

The treatment response of barrier membrane with amoxicillin-loaded nanofibers in experimental periodontitis.

BACKGROUND: Infection control is a major determinant of guided tissue regeneration (GTR). This study aims to develop an antibiotic-loaded membrane to assist periodontal repair. METHODS: Poly(D,L-lactic acid) (PDLLA) nanofibers encapsulating amoxicillin (PDLLA-AMX) were fabricated using the electrospinning technique, and their structures, drug encapsulation efficiency, and release characteristics were assessed. The viability and behaviors of periodontal ligament (PDL) cells on nanofibers, and antibacterial capabilities of nanofibers were evaluated in vitro. Early therapeutic efficiency of the antibiotic-loaded membranes was investigated in rats with ligature-induced experimental periodontitis, and the outcomes were evaluated by gene expression, microcomputed tomography imaging, and histology within 7 days of membrane placement. RESULTS: AMX was successfully encapsulated in the PDLLA nanofibers and released in a sustained manner. After initial attachment was achieved, cells stretched out along with the directions of nanofibers. The viability and expression of migration-associated gene of PDL cells were significantly improved, and the growth of Streptococcus sanguinis and Porphyromonas gingivalis was significantly reduced in the PDLLA-AMX group compared with the controls. On PDLLA-AMX-treated sites, wound dehiscence and sulcular inflammation were reduced. Collagen fiber matrix deposition was accelerated with upregulated type I collagen and interleukin-1ß, and downregulated matrix metalloproteinase-8, whereas periodontal bone level and the expressions of vascular endothelial growth factor and core-binding factor subunit alpha-1 were equivalent to conventional membrane treatment. CONCLUSIONS: PDLLA-AMX nanofibers inhibited bacterial growth and promoted the viability and mobility of PDL cells after initial cell attachment. Membranes with PDLLA-AMX nanofibers reduced inflammation and accelerated periodontal repair at an early stage, providing good prospects for the further development of GTR membranes.

Spatiotemporal segregation of human marginal zone and memory B cell populations in lymphoid tissue.

Human memory B cells and marginal zone (MZ) B cells share common features such as the expression of CD27 and somatic mutations in their IGHV and BCL6 genes, but the relationship between them is controversial. Here, we show phenotypic progression within lymphoid tissues as MZ B cells emerge from the mature naïve B cell pool via a precursor CD27-CD45RBMEM55+ population distant from memory cells. By imaging mass cytometry, we find that MZ B cells and memory B cells occupy different microanatomical niches in organised gut lymphoid tissues. Both populations disseminate widely between distant lymphoid tissues and blood, and both diversify their IGHV repertoire in gut germinal centres (GC), but nevertheless remain largely clonally separate. MZ B cells are therefore not developmentally contiguous with or analogous to classical memory B cells despite their shared ability to transit through GC, where somatic mutations are acquired.

IgE repertoire and immunological memory: compartmental regulation and antibody function.

It is now generally recognized that bone marrow is the survival niche for antigen-specific plasma cells with long-term immunological memory. These cells release antibodies into the circulation, needed to prime effector cells in the secondary immune response. These antibodies participate in the surveillance for antigen and afford immune defence against pathogens and toxins previously encountered in the primary immune response. IgE antibodies function together with their effector cells, mast cells, to exert 'immediate hypersensitivity' in mucosal tissues at the front line of immune defence. The constant supply of IgE antibodies from bone marrow plasma cells allows the rapid 'recall response' by mast cells upon re-exposure to antigen even after periods of antigen absence. The speed and sensitivity of the IgE recall response and potency of the effector cell functions are advantageous in the early detection and elimination of pathogens and toxins at the sites of attack. Local antigen provocation also stimulates de novo synthesis of IgE or its precursors of other isotypes that undergo IgE switching in the mucosa. This process, however, introduces a delay before mast cells can be sensitized and resume activity; this is terminated shortly after the antigen is eliminated. Recent results from adaptive immune receptor repertoire sequencing of immunoglobulin genes suggest that the mucosal IgE+ plasmablasts, which have undergone affinity maturation in the course of their evolution in vivo, are a source of long-lived IgE+ plasma cells in the bone marrow that are already fully functional.

Antibodies and superantibodies in patients with chronic rhinosinusitis with nasal polyps.

BACKGROUND: Chronic rhinosinusitis with nasal polyps is associated with local immunoglobulin hyperproduction and the presence of IgE antibodies against Staphylococcus aureus enterotoxins (SAEs). Aspirin-exacerbated respiratory disease is a severe form of chronic rhinosinusitis with nasal polyps in which nearly all patients express anti-SAEs. OBJECTIVES: We aimed to understand antibodies reactive to SAEs and determine whether they recognize SAEs through their complementarity-determining regions (CDRs) or framework regions. METHODS: Labeled staphylococcal enterotoxin (SE) A, SED, and SEE were used to isolate single SAE-specific B cells from the nasal polyps of 3 patients with aspirin-exacerbated respiratory disease by using fluorescence-activated cell sorting. Recombinant antibodies with "matched" heavy and light chains were cloned as IgG1, and those of high affinity for specific SAEs, assayed by means of ELISA and surface plasmon resonance, were recloned as IgE and antigen-binding fragments. IgE activities were tested in basophil degranulation assays. RESULTS: Thirty-seven SAE-specific, IgG- or IgA-expressing B cells were isolated and yielded 6 anti-SAE clones, 2 each for SEA, SED, and SEE. Competition binding assays revealed that the anti-SEE antibodies recognize nonoverlapping epitopes in SEE. Unexpectedly, each anti-SEE mediated SEE-induced basophil degranulation, and IgG1 or antigen-binding fragments of each anti-SEE enhanced degranulation by the other anti-SEE. CONCLUSIONS: SEEs can activate basophils by simultaneously binding as antigens in the conventional manner to CDRs and as superantigens to framework regions of anti-SEE IgE in anti-SEE IgE-FcεRI complexes. Anti-SEE IgG1s can enhance the activity of anti-SEE IgEs as conventional antibodies through CDRs or simultaneously as conventional antibodies and as "superantibodies" through CDRs and framework regions to SEEs in SEE-anti-SEE IgE-FcεRI complexes.

Transitional B Cells in Early Human B Cell Development - Time to Revisit the Paradigm?

The B cell repertoire is generated in the adult bone marrow by an ordered series of gene rearrangement processes that result in massive diversity of immunoglobulin (Ig) genes and consequently an equally large number of potential specificities for antigen. As the process is essentially random, the cells exhibiting excess reactivity with self-antigens are generated and need to be removed from the repertoire before the cells are fully mature. Some of the cells are deleted, and some will undergo receptor editing to see if changing the light chain can rescue an autoreactive antibody. As a consequence, the binding properties of the B cell receptor are changed as development progresses through pre-B ≫ immature ≫ transitional ≫ naïve phenotypes. Using long-read, high-throughput, sequencing we have produced a unique set of sequences from these four cell types in human bone marrow and matched peripheral blood, and our results describe the effects of tolerance selection on the B cell repertoire at the Ig gene level. Most strong effects of selection are seen within the heavy chain repertoire and can be seen both in gene usage and in CDRH3 characteristics. Age-related changes are small, and only the size of the CDRH3 shows constant and significant change in these data. The paucity of significant changes in either kappa or lambda light chain repertoires implies that either the heavy chain has more influence over autoreactivity than light chain and/or that switching between kappa and lambda light chains, as opposed to switching within the light chain loci, may effect a more successful autoreactive rescue by receptor editing. Our results show that the transitional cell population contains cells other than those that are part of the pre-B ≫ immature ≫ transitional ≫ naïve development pathway, since the population often shows a repertoire that is outside the trajectory of gene loss/gain between pre-B and naïve stages.

Significant Differences in Physicochemical Properties of Human Immunoglobulin Kappa and Lambda CDR3 Regions.

Antibody variable regions are composed of a heavy and a light chain, and in humans, there are two light chain isotypes: kappa and lambda. Despite their importance in receptor editing, the light chain is often overlooked in the antibody literature, with the focus being on the heavy chain complementarity-determining region (CDR)-H3 region. In this paper, we set out to investigate the physicochemical and structural differences between human kappa and lambda light chain CDR regions. We constructed a dataset containing over 29,000 light chain variable region sequences from IgM-transcribing, newly formed B cells isolated from human bone marrow and peripheral blood. We also used a published human naïve dataset to investigate the CDR-H3 properties of heavy chains paired with kappa and lambda light chains and probed the Protein Data Bank to investigate the structural differences between kappa and lambda antibody CDR regions. We found that kappa and lambda light chains have very different CDR physicochemical and structural properties, whereas the heavy chains with which they are paired do not differ significantly. We also observed that the mean CDR3 N nucleotide addition in the kappa, lambda, and heavy chain gene rearrangements are correlated within donors but can differ between donors. This indicates that terminal deoxynucleotidyl transferase may work with differing efficiencies between different people but the same efficiency in the different classes of immunoglobulin chain within one person. We have observed large differences in the physicochemical and structural properties of kappa and lambda light chain CDR regions. This may reflect different roles in the humoral immune response.