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.

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.

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.

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.

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).

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.

Influence of seasonal exposure to grass pollen on local and peripheral blood IgE repertoires in patients with allergic rhinitis.

BACKGROUND: Previous studies of immunoglobulin gene sequences in patients with allergic diseases using low-throughput Sanger sequencing have limited the analytic depth for characterization of IgE repertoires. OBJECTIVES: We used a high-throughput, next-generation sequencing approach to characterize immunoglobulin heavy-chain gene (IGH) repertoires in patients with seasonal allergic rhinitis (AR) with the aim of better understanding the underlying disease mechanisms. METHODS: IGH sequences in matched peripheral blood and nasal biopsy specimens from nonallergic healthy control subjects (n = 3) and patients with grass pollen-related AR taken in season (n = 3) or out of season (n = 4) were amplified and pyrosequenced on the 454 GS FLX+ System. RESULTS: A total of 97,610 IGH (including 8,135 IgE) sequences were analyzed. Use of immunoglobulin heavy-chain variable region gene families 1 (IGHV1) and 5 (IGHV5) was higher in IgE clonotypic repertoires compared with other antibody classes independent of atopic status. IgE repertoires measured inside the grass pollen season were more diverse and more mutated (particularly in the biopsy specimens) and had more evidence of antigen-driven selection compared with those taken outside of the pollen season or from healthy control subjects. Clonal relatedness was observed for IgE between the blood and nasal biopsy specimens. Furthermore in patients with AR, but not healthy control subjects, we found clonal relatedness between IgE and IgG classes. CONCLUSION: This is the first report that exploits next-generation sequencing to determine local and peripheral blood IGH repertoires in patients with respiratory allergic disease. We demonstrate that natural pollen exposure was associated with changes in IgE repertoires that were suggestive of ongoing germinal center reactions. Furthermore, these changes were more often apparent in nasal biopsy specimens compared with peripheral blood and in patients with AR compared with healthy control subjects.

Tunable microwave generation of a monolithic dual-wavelength distributed feedback laser.

The dynamic behavior of a monolithic dual-wavelength distributed feedback laser was fully investigated and mapped. The combination of different driving currents for master and slave lasers can generate a wide range of different operational modes, from single mode, period 1 to chaos. Both the optical and microwave spectrum were recorded and analyzed. The detected single mode signal can continuously cover from 15GHz to 50GHz, limited by photodetector bandwidth. The measured optical four-wave-mixing pattern indicates that a 70GHz signal can be generated by this device. By applying rate equation analysis, the important laser parameters can be extracted from the spectrum. The extracted relaxation resonant frequency is found to be 8.96GHz. With the full operational map at hand, the suitable current combination can be applied to the device for proper applications.

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.

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.

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.

High-throughput immunoglobulin repertoire analysis distinguishes between human IgM memory and switched memory B-cell populations.

B-cell receptor (BCR) diversity is achieved centrally by rearrangement of Variable, Diversity, and Joining genes, and peripherally by somatic hypermutation and class-switching of the rearranged genes. Peripheral B-cell populations are subject to both negative and positive selection events in the course of their development that have the potential to shape the BCR repertoire. The origin of IgM(+)IgD(+)CD27(+) (IgM memory) cells is controversial. It has been suggested that they may be a prediversified, antigen-independent, population of cells or that they are a population of cells that develop in response to T-independent antigens. Most recently, it was suggested that the majority of IgM memory cells are directly related to switched memory cells and are early emigrants from the germinal center reaction. Advances in sequencing technology have enabled us to undertake large scale IGH repertoire analysis of transitional, naive, IgM memory and switched memory B-cell populations. We find that the memory B-cell repertoires differ from the transitional and naive repertoires, and that the IgM memory repertoire is distinct from that of class-switched memory. Thus we conclude that a large proportion of IgM memory cells develop in response to different stimuli than for class-switched memory cell development.

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.

B cell immunosenescence: different features of naive and memory B cells in elderly.

Elderly people show a reduced protection against new infections and a decreased response to vaccines as a consequence of impairment of both cellular and humoral immunity. In this paper we have studied memory/naïve B cells in the elderly, evaluating surface immunoglobulin expression, production of the pro- and anti-inflammatory cytokines, tumor necrosis factor (TNF)-α and interleukin (IL)-10, and presence of somatic hypermutation, focusing on the IgG(+)IgD(-)CD27(-) double negative (DN) B cells that are expanded in the elderly. Our results show that naïve B cells from young donors need a sufficiently strong stimulus to be activated "in vitro", while naïve B cells from old subjects are able to produce IL-10 and TNF-α when stimulated "physiologically" (α-CD40/IL-4), suggesting that these cells might play a role in the control of the immuno-inflammatory environment in the elderly. In addition, in the elderly there is an accumulation of DN B cells with a reduced rate of somatic hypermutation. Thus, DN B lymphocytes may be exhausted cells that are expanded and accumulate as a by-product of persistent stimulation or impaired germinal center formation.

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.

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.

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.