Fast heritability estimation based on MINQUE and batch training.

Heritability, the proportion of phenotypic variance explained by genome-wide single nucleotide polymorphisms (SNPs) in unrelated individuals, is an important measure of the genetic contribution to human diseases and plays a critical role in studying the genetic architecture of human diseases. Linear mixed model (LMM) has been widely used for SNP heritability estimation, where variance component parameters are commonly estimated by using a restricted maximum likelihood (REML) method. REML is an iterative optimization algorithm, which is computationally intensive when applied to large-scale datasets (e.g. UK Biobank). To facilitate the heritability analysis of large-scale genetic datasets, we develop a fast approach, minimum norm quadratic unbiased estimator (MINQUE) with batch training, to estimate variance components from LMM (LMM.MNQ.BCH). In LMM.MNQ.BCH, the parameters are estimated by MINQUE, which has a closed-form solution for fast computation and has no convergence issue. Batch training has also been adopted in LMM.MNQ.BCH to accelerate the computation for large-scale genetic datasets. Through simulations and real data analysis, we demonstrate that LMM.MNQ.BCH is much faster than two existing approaches, GCTA and BOLT-REML.

A review of SNP heritability estimation methods.

Over the past decade, statistical methods have been developed to estimate single nucleotide polymorphism (SNP) heritability, which measures the proportion of phenotypic variance explained by all measured SNPs in the data. Estimates of SNP heritability measure the degree to which the available genetic variants influence phenotypes and improve our understanding of the genetic architecture of complex phenotypes. In this article, we review the recently developed and commonly used SNP heritability estimation methods for continuous and binary phenotypes from the perspective of model assumptions and parameter optimization. We primarily focus on their capacity to handle multiple phenotypes and longitudinal measurements, their ability for SNP heritability partition and their use of individual-level data versus summary statistics. State-of-the-art statistical methods that are scalable to the UK Biobank dataset are also elucidated in detail.

Heritability of cognitive abilities and regional brain structures in middle-aged to elderly East Asians.

This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer's and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.

SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships.

BACKGROUND: Heritability and genetic correlation can be estimated from genome-wide single-nucleotide polymorphism (SNP) data using various methods. We recently developed multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) for statistically and computationally efficient estimation of SNP-based heritability ([Formula: see text]) and genetic correlation ([Formula: see text]) across many traits in large datasets. Here, we extend MGREML by allowing it to fit and perform tests on user-specified factor models, while preserving the low computational complexity. RESULTS: Using simulations, we show that MGREML yields consistent estimates and valid inferences for such factor models at low computational cost (e.g., for data on 50 traits and 20,000 individuals, a saturated model involving 50 [Formula: see text]'s, 1225 [Formula: see text]'s, and 50 fixed effects is estimated and compared to a restricted model in less than one hour on a single notebook with two 2.7 GHz cores and 16 GB of RAM). Using repeated measures of height and body mass index from the US Health and Retirement Study, we illustrate the ability of MGREML to estimate a factor model and test whether it fits the data better than a nested model. The MGREML tool, the simulation code, and an extensive tutorial are freely available at https://github.com/devlaming/mgreml/ . CONCLUSION: MGREML can now be used to estimate multivariate factor structures and perform inferences on such factor models at low computational cost. This new feature enables simple structural equation modeling using MGREML, allowing researchers to specify, estimate, and compare genetic factor models of their choosing using SNP data.

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood.

Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

Introducing M-GCTA a Software Package to Estimate Maternal (or Paternal) Genetic Effects on Offspring Phenotypes.

There is increasing interest within the genetics community in estimating the relative contribution of parental genetic effects on offspring phenotypes. Here we describe the user-friendly M-GCTA software package used to estimate the proportion of phenotypic variance explained by maternal (or alternatively paternal) and offspring genotypes on offspring phenotypes. The tool requires large studies where genome-wide genotype data are available on mother- (or alternatively father-) offspring pairs. The software includes several options for data cleaning and quality control, including the ability to detect and automatically remove cryptically related pairs of individuals. It also allows users to construct genetic relationship matrices indexing genetic similarity across the genome between parents and offspring, enabling the estimation of variance explained by maternal (or alternatively paternal) and offspring genetic effects. We evaluated the performance of the software using a range of data simulations and estimated the computing time and memory requirements. We demonstrate the use of M-GCTA on previously analyzed birth weight data from two large population based birth cohorts, the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Norwegian Mother and Child Cohort Study (MoBa). We show how genetic variation in birth weight is predominantly explained by fetal genetic rather than maternal genetic sources of variation.

Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies.

Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer's Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.

Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity.

Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but current understanding of specific genetic influences remains limited. We performed the largest genome-wide association study to date of oscillatory power during eyes-closed resting electroencephalogram (EEG) across a range of frequencies (delta 1-3.75 Hz, theta 4-7.75 Hz, alpha 8-12.75 Hz, and beta 13-30 Hz) in 8,425 subjects. Additionally, we performed KGG positional gene-based analysis and brain-expression analyses. GABRA2-a known genetic marker for alcohol use disorder and epilepsy-significantly affected beta power, consistent with the known relation between GABAA interneuron activity and beta oscillations. Tissue-specific SNP-based imputation of gene-expression levels based on the GTEx database revealed that hippocampal GABRA2 expression may mediate this effect. Twenty-four genes at 3p21.1 were significant for alpha power (FDR q < .05). SNPs in this region were linked to expression of GLYCTK in hippocampal tissue, and GNL3 and ITIH4 in the frontal cortex-genes that were previously implicated in schizophrenia and bipolar disorder. In sum, we identified several novel genetic variants associated with oscillatory brain activity; furthermore, we replicated and advanced understanding of previously known genes associated with psychopathology (i.e., schizophrenia and alcohol use disorders). Importantly, these psychopathological liability genes affect brain functioning, linking the genes' expression to specific cortical/subcortical brain regions.

Phenotypic and Genetic Relationship Between BMI and Drinking in a Sample of UK Adults.

The health impairments derived from both alcoholism and obesity are well known. However, reports that relate increased alcohol use with increased measures of obesity have been mixed in their findings, especially with respect to genetic factors that could potentially link these two behaviors. Here, using a large sample of adults from the UK (n ≈ 113,000), we report both the observed and genetic correlations between BMI (kg/m2) and two measures of alcohol use: reported quantity (drinks per week) and frequency of use (from never to daily). Overall, both observationally and genetically, alcohol intake is negatively correlated with BMI. Phenotypic correlations ranged from -0.01 to -0.17, and genetic correlations ranged from -0.1 to -0.4. Genetic correlations tended to be stronger than the phenotypic correlations, and these correlations were stronger in females and between BMI and, specifically, frequency of use. Though the mechanisms driving these relationships are yet to be identified, we can conclude that the genetic factors related to drinking both more and more often are shared with those responsible for lower BMI.

Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses.

BACKGROUND: Alcohol dependence (AD) is a complex psychiatric disorder and a significant public health problem. Twin and family-based studies have consistently estimated its heritability to be approximately 50%, and many studies have sought to identify specific genetic variants associated with susceptibility to AD. These studies have been primarily linkage or candidate gene based and have been mostly unsuccessful in identifying replicable risk loci. Genome-wide association studies (GWAS) have improved the detection of specific loci associated with complex traits, including AD. However, findings from GWAS explain only a small proportion of phenotypic variance, and alternative methods have been proposed to investigate the associations that do not meet strict genome-wide significance criteria. METHODS: This review summarizes all published AD GWAS and post-GWAS analyses that have sought to exploit GWAS data to identify AD-associated loci. RESULTS: Findings from AD GWAS have been largely inconsistent, with the exception of variants encoding the alcohol-metabolizing enzymes. Analyses of GWAS data that go beyond standard association testing have demonstrated the polygenic nature of AD and the large contribution of common variants to risk, nominating novel genes and pathways for AD susceptibility. CONCLUSIONS: Findings from AD GWAS and post-GWAS analyses have greatly increased our understanding of the genetic etiology of AD. However, it is clear that larger samples will be necessary to detect loci in addition to those that encode alcohol-metabolizing enzymes, which may only be possible through consortium-based efforts. Post-GWAS approaches to studying the genetic influences on AD are increasingly common and could greatly increase our knowledge of both the genetic architecture of AD and the specific genes and pathways that influence risk.

Genetic and brain similarity independently predict childhood anthropometrics and neighborhood socioeconomic conditions.

Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ an integrative approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, associations between cortical thickness similarity and traits were limited to anthropometrics such as height, weight, and birth weight, as well as a marker of neighborhood socioeconomic conditions. Common genetic variants explained significant proportions of variance across nearly all included outcomes, although estimates were somewhat lower than previous reports. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as neighborhood socioeconomic conditions and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample.

Recent Advances in Genomic Studies of Gestational Duration and Preterm Birth.

Preterm birth (PTB) is the leading cause of infant mortality and morbidity. For several decades, extensive epidemiologic and genetic studies have highlighted the significant contribution of maternal and offspring genetic factors to PTB. This review discusses the challenges inherent in conventional genomic analyses of PTB and underscores the importance of adopting nonconventional approaches, such as analyzing the mother-child pair as a single analytical unit, to disentangle the intertwined maternal and fetal genetic influences. We elaborate on studies investigating PTB phenotypes through 3 levels of genetic analyses: single-variant, multi-variant, and genome-wide variants.

Heritability Estimation Approaches Utilizing Genome-Wide Data.

Prior to the development of genome-wide arrays and whole genome sequencing technologies, heritability estimation mainly relied on the study of related individuals. Over the past decade, various approaches have been developed to estimate SNP-based narrow-sense heritability ( h SNP 2 ${\rm{h}}_{{\rm{SNP}}}^2$ ) in unrelated individuals. These latter approaches use either individual-level genetic variations or summary results from genome-wide association studies (GWAS). Recently, several studies compared these approaches using extensive simulations and empirical datasets. However, sparse information on hands-on training necessitates revisiting these approaches from the perspective of a stepwise guide for practical applications. Here, we provide an overview of the commonly used SNP-heritability estimation approaches utilizing genome-wide array, imputed or whole genome data from unrelated individuals, or summary results. We not only discuss these approaches based on their statistical concepts, utility, advantages, and limitations, but also provide step-by-step protocols to apply these approaches. For illustration purposes, we estimate h SNP 2 ${\rm{h}}_{{\rm{SNP}}}^2$ of height and BMI utilizing individual-level data from The Northern Finland Birth Cohort (NFBC) and summary results from the Genetic Investigation of ANthropometric Traits (GIANT;) consortium. We present this review as a template for the researchers who estimate and use heritability in their studies and as a reference for geneticists who develop or extend heritability estimation approaches. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: GREML (GCTA) Alternate Protocol 1: Stratified GREML Basic Protocol 2: LDAK Alternate Protocol 2: Stratified LDAK Basic Protocol 3: Threshold GREML Basic Protocol 4: LD score (LDSC) regression Basic Protocol 5: SumHer.

Identifying Genes Associated with Alzheimer's Disease Using Gene-Based Polygenic Risk Score.

BACKGROUND: Except APOE, Alzheimer's disease (AD) associated genes identified in recent large-scale genome-wide association studies (GWAS) had small effects and explained a small portion of heritability. Many AD-associated genes have even smaller effects thereby sub-threshold p-values in large-scale GWAS and remain to be identified. For some AD-associated genes, drug targeting them may have limited efficacies due to their small effect sizes. OBJECTIVE: The purpose of this study is to identify AD-associated genes with sub-threshold p-values and prioritize drugs targeting AD-associated genes that have large efficacies. METHODS: We developed a gene-based polygenic risk score (PRS) to identify AD genes. It was calculated using SNPs located within genes and having the same directions of effects in different study cohorts to exclude cohort-specific findings and false positives. Gene co-expression modules and protein-protein interaction networks were used to identify AD-associated genes that interact with multiple other genes, as drugs targeting them have large efficacies via co-regulation or interactions. RESULTS: Gene-based PRS identified 389 genes with 164 of them not previously reported as AD-associated. These 389 genes explained 56.12% -97.46% SNP heritability; and they were enriched in brain tissues and 164 biological processes, most of which are related to AD and other neurodegenerative diseases. We prioritized 688 drugs targeting 64 genes that were in the same co-expression modules and/or PPI networks. CONCLUSIONS: Gene-based PRS is a cost-effective way to identify AD-associated genes without substantially increasing the sample size. Co-expression modules and PPI networks can be used to identify drugs having large efficacies.

Statistical methods for SNP heritability estimation and partition: A review.

In GWAS studies, SNP heritability measures the proportion of phenotypic variance explained by all measured SNPs. Accurate estimation of SNP heritability can help us better understand the degree to which measured genetic variants influence phenotypes. Over the last decade, a variety of statistical methods and software tools have been developed for SNP heritability estimation with different data types including genotype array data, imputed genotype data, whole-genome sequencing data, RNA sequencing data, and bisulfite sequencing data. However, a thorough technical review of these methods, especially from a statistical and computational viewpoint, is currently missing. To fill this knowledge gap, we present a comprehensive review on a broad category of recently developed and commonly used SNP heritability estimation methods. We focus on their modeling assumptions; their interconnected relationships; their applicability to quantitative, binary and count phenotypes; their use of individual level data versus summary statistics, as well as their utility for SNP heritability partitioning. We hope that this review will serve as a useful reference for both methodologists who develop heritability estimation methods and practitioners who perform heritability analysis.

A Between Ethnicities Comparison of Chronic Obstructive Pulmonary Disease Genetic Risk.

Heterogeneity of lung function levels and risk for developing chronic obstructive pulmonary disease (COPD) among people exposed to the same environmental risk factors, such as cigarette smoking, suggest an important role of genetic factors in COPD susceptibility. To investigate the possible role of different genetic factors in COPD susceptibility across ethnicities. We used a population-stratified analysis for: (i) identifying ethnic-specific genetic susceptibility loci, (ii) developing ethnic-specific polygenic risk prediction models using those SNPs, and (iii) validating the models with an independent dataset. We elucidated substantial differences in SNP heritability and susceptibility loci for the disease across ethnicities. Furthermore, the application of three ethnic-specific prediction models to an independent dataset showed that the best performance is achieved when the prediction model is applied to a dataset with the matched ethnic sample. Our study validates the necessity of considering ethnic differences in COPD risk; understanding these differences might help in preventing COPD and developing therapeutic strategies.

Phenotypic and genetic relationship between BMI and cigarette smoking in a sample of UK adults.

In addition to the health hazards posed individually by cigarette smoking and obesity, the combination of these conditions poses a particular impairment to health. Genetic factors have been shown to influence both traits and, to understand the connection between these conditions, we examined both the observed and genetic relationship between adiposity (an electrical impedance measure of body mass index (BMI)) and cigarettes smoked per day (CPD) in a large sample of current, former, and never smokers in the United Kingdom. In former smokers, BMI was positively associated with cigarettes formerly smoked; further, the genetic factors related to a greater number of cigarettes smoked were also responsible for a higher BMI. In current smokers, there was a positive association between BMI and number of cigarettes smoked, though this relationship did not appear to be influenced by similar genetic factors. We found a positive genetic relationship between smoking in current/former smokers and BMI in never smokers (who would be unmarred by the effects of nicotine). In addition to CPD, in current smokers, we looked at two variables, time from waking to first cigarette and difficulty not smoking for a day, that may align better with cigarette and food 'craving.' However, these smoking measures provided mixed findings with respect to their relationship with BMI. Overall, the positive relationships between the genetic factors that influence CPD in smokers and the genetic factors that influence BMI in former and never smokers point to common biological influences behind smoking and obesity.

Accounting for heteroscedasticity and censoring in chromosome partitioning analyses.

A fundamental assumption in quantitative genetics is that traits are controlled by many loci of small effect. Using genomic data, this assumption can be tested using chromosome partitioning analyses, where the proportion of genetic variance for a trait explained by each chromosome (h2 c ), is regressed on its size. However, as h2 c -estimates are necessarily positive (censoring) and the variance increases with chromosome size (heteroscedasticity), two fundamental assumptions of ordinary least squares (OLS) regression are violated. Using simulated and empirical data we demonstrate that these violations lead to incorrect inference of genetic architecture. The degree of bias depends mainly on the number of chromosomes and their size distribution and is therefore specific to the species; using published data across many different species we estimate that not accounting for this effect overall resulted in 28% false positives. We introduce a new and computationally efficient resampling method that corrects for inflation caused by heteroscedasticity and censoring and that works under a large range of dataset sizes and genetic architectures in empirical datasets. Our new method substantially improves the robustness of inferences from chromosome partitioning analyses.

Single Nucleotide Polymorphism Heritability of a General Psychopathology Factor in Children.

OBJECTIVE: Co-occurrence of mental disorders is commonly observed, but the etiology underlying this observation is poorly understood. Studies in adolescents and adults have identified a general psychopathology factor associated with a high risk for different psychiatric disorders. We defined a multi-informant general psychopathology factor in school-aged children and estimated its single nucleotide polymorphism (SNP) heritability. The goal was to test the hypothesis that child behavioral and emotional problems are under the influence of highly pleiotropic common autosomal genetic variants that nonspecifically increase the risk for different dimensions of psychopathology. METHOD: Children from the Generation R cohort were repeatedly assessed between ages 6 to 8 years. Child behavior problems were reported by parents, teachers, and children. Confirmatory factor analysis estimated a general psychopathology factor across informants using various psychiatric problem scales. Validation of the general psychopathology factor was based on IQ and temperamental measures. Genome-wide complex trait analysis (GCTA) was used to estimate the SNP heritability (N = 2,115). RESULTS: The general psychopathology factor was associated with lower IQ, higher negative affectivity, and lower effortful control, but not with surgency. Importantly, the general psychopathology factor showed a significant SNP heritability of 38% (SE = 0.16, p = .008). CONCLUSION: Common autosomal SNPs are pleiotropically associated with internalizing, externalizing, and other child behavior problems, and underlie a general psychopathology factor in childhood.