Biology subjects

Keller, M. C.

Publications and source records attributed to Keller, M. C..

14 recordsLinked to original sources

bioRxiv2026

Near perfect identification of half sibling versus niece/nephew avuncular pairs without pedigree information or genotyped relatives

Motivation: Large-scale genomic biobanks contain thousands of second-degree relatives with missing pedigree metadata. Accurately distinguishing half-sibling (HS) from niece/nephew-avuncular (N/A) pairs--both sharing approximately 25% of the genome--remains a significant challenge. Current SNP-based methods rely on Identical-By-Descent (IBD) segment counts and age differences, but substantial distributional overlap leads to high misclassification rates. There is a critical need for a scalable, genotype-only method that can resolve these "half-degree" ambiguities without requiring observed pedigrees or extensive relative information. Results: We present a novel computational framework that achieves near-complete separation of HS and N/A pairs using only genotype data. Our approach utilizes across-chromosome phasing to derive haplotype-level sharing features that summarize how IBD is distributed across parental homologues. By modeling these features with a Gaussian mixture model (GMM), we demonstrate near-perfect classification accuracy (> 98%) in biobank-scale data. Furthermore, we show that these high-confidence relationship labels can serve as long-range phasing anchors, providing structural constraints that improve the accuracy of across-chromosome homologue assignment. This method provides a robust, scalable solution for pedigree reconstruction and the control of cryptic relatedness in large-scale genomic studies.

bioinformatics↗

bioRxiv2022

Declining autozygosity over time: an exploration in over 1 million individuals from three diverse cohorts

We hypothesized that overall autozygosity is decreasing over generational time. In this report, we present data that partially support this hypothesis from three large cohorts of diverse ancestries, two from the US (All of Us and the Million Veteran Program, N=82,474 and 622,497, respectively) and one from the UK (UK Biobank, N=380,899). Our results from a mixed-effect meta-analysis demonstrate an overall trend of decreasing autozygosity over generational time (meta-analyzed slope=-0.029, se=0.009, p=6.03e-4). Using a chi-square difference test, we determined that a model including an ancestry-by-country interaction term fit the data best, indicating that ancestry differences in this trend differ by country. We found further evidence to suggest a difference between the US and UK cohorts by meta-analyzing within country, observing a significant negative estimate in the US cohorts (meta-analyzed slope=-0.058, se=0.015, p=1.50e-4) but a non-significant estimate in the UK (meta-analyzed slope=-0.001, se=0.008, p=0.945). We also found that the association between autozygosity and year of birth in the overall meta-analysis was substantially attenuated when accounting for educational attainment and income (meta-analyzed slope=-0.011, se=0.008, p=0.167), suggesting that increases in education and income may partially account for decreasing levels of autozygosity over time. To our knowledge, this is the largest demonstration of decreasing autozygosity over time in a modern sample (birth years 1904-2003), and we speculate that this trend can be attributed to increases in population size, urbanization and panmixia, with differences in demographic and sociocultural processes leading to country-specific differences in the rate of decline.

genomics↗

bioRxiv2022

A comprehensive meta-analysis of human assortative mating in 22 complex traits

Positive correlations between human mating partners are consistently observed across traits. Such correlations can increase phenotypic variation and, to the extent that they reflect genetic similarity in co-parents, can also increase prevalence for rare phenotypes and bias estimates in genetic designs. We conducted the largest set of meta-analyses on human partner correlations to date, incorporating 480 partner correlations across 22 traits. We also calculated 133 trait correlations between up to 79,074 male-female couples in the UK Biobank (UKB). Estimates of the mean meta-analyzed correlations ranged from rmeta=.08 for extraversion to rmeta=.58 for political values. UKB correlations ranged from rUKB=-.18 for chronotype to rUKB=.87 for birth year. Overall, attitudes, education, and substance use traits mostly showed the highest correlations, while psychological and biological traits generally yielded lower but still positive correlations. We observed high between-study heterogeneity for most meta-analyzed traits, likely because of both systematic differences between samples and true differences in partner correlations across populations.

genetics↗

bioRxiv2021

Exploring the relationships between autozygosity, educational attainment, and cognitive ability in a contemporary, trans-ancestral American sample

Previous studies have found significant associations between estimated autozygosity - the proportion of an individuals genome contained in homozygous segments due to distant inbreeding - and multiple traits, including educational attainment (EA) and cognitive ability. In one study, estimated autozygosity showed a stronger association with parental EA than the subjects own EA. This was likely driven by parental EAs association with mobility: more educated parents tended to migrate further from their hometown, therefore choosing more genetically diverse partners. We examined the associations between estimated autozygosity, cognitive ability, and parental EA in a contemporary sub-sample of adolescents from the Adolescent Brain and Cognitive Development StudySM (ABCD Study(R)) (analytic N=6,504). We found a negative association between autozygosity and child cognitive ability consistent with previous studies, while the associations between autozygosity and parental EA were in the expected direction of effect (with greater levels of autozygosity being associated with lower EA) but the effect sizes were significantly weaker than those estimated in previous work. We also found a lower mean level of autozygosity in the ABCD sample compared to previous autozygosity studies, which may reflect overall decreasing levels of autozygosity over generations. Variation in migration and mobility patterns in the ABCD study compared to other studies may explain the pattern of associations between estimated autozygosity, EA, and cognitive ability in the current study.

genetics↗

bioRxiv2021

Assortative Mating Biases Marker-based Heritability Estimators

Many complex traits are subject to assortative mating (AM), with recent molecular genetic findings confirming longstanding theoretical predictions that AM alters genetic architecture by inducing long range dependence across causal variants. However, all marker-based heritability estimators assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments estimators and likelihood-based estimators produce biased estimates in the presence of AM and that common approaches to account for population structure fail to mitigate this bias. Then, examining height and educational attainment in the UK Biobank, we demonstrate that these biases affect real world traits. Finally, we derive corrected heritability estimators for traits under equilibrium AM.

genetics↗

bioRxiv2020

Bias and precision of parameter estimates from models using polygenic scores to estimate environmental and genetic parental influences

In a companion paper (Balbona et al. (2020)), we introduced a series of causal models that use polygenic scores from transmitted and nontransmitted alleles, the offspring trait, and parental traits to estimate the variation due to the environmental influences the parental trait has on the offspring trait (vertical transmission) as well as additive genetic effects. These models also estimate and account for the gene-gene and gene-environment covariation that arises from assortative mating and vertical transmission respectively. In the current study, we simulated polygenic scores and phenotypes of parents and offspring under genetic and vertical transmission scenarios, assuming two types of assortative mating. We instantiated the models from our companion paper in the OpenMx software, and compared the true values of parameters to maximum likelihood estimates from models fitted on the simulated data to quantify the bias and precision of estimates. We show that parameter estimates from these models are unbiased when assumptions are met, but as expected, they are biased to the degree that assumptions are unmet. Standard errors of the estimated variances due to vertical transmission and to genetic effects decrease with increasing sample sizes and with increasing r2 values of the polygenic score. Even when the polygenic score explains a modest amount of trait variation (r2 = .05), standard errors of these standardized estimates were reasonable (< .05) for n = 16K trios, and smaller sample sizes (e.g., down to 4K) when the polygenic score is more predictive. These causal models offer a novel approach for understanding how parents influence their offspring, but their use requires polygenic scores on relevant traits that are modestly predictive (e.g., r2 > .025) as well as datasets with genomic and phenotypic information on parents and offspring. The utility of polygenic scores for elucidating parental influences should thus serve as additional motivation for large genomic biobanks to perform GWASs on traits that may be relevant to parenting and to oversample close relatives, particularly parents and offspring.

genetics↗

bioRxiv2020

Estimation of parental effects using polygenic scores

Offspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents nontransmitted alleles, one can estimate the effect of "genetic nurture"-- a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to a) account for the influence of assortative mating at both equilibrium and disequilibrium (after a single generation of assortment), and b) include measured parental phenotypes, allowing for the estimation of the total variation due to additive genetic effects and their covariance with the familial environment. By utilizing path analysis techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.

genetics↗

bioRxiv2019

The role of a priori-identified addiction and smoking gene sets in smoking behaviors.

IntroductionSmoking is a leading cause of death, and genetic variation contributes to smoking behaviors. Identifying genes and sets of genes that contribute to risk for addiction is necessary to prioritize targets for functional characterization and for personalized medicine.\n\nMethodsWe performed a gene set-based association and heritable enrichment study of two addiction-related gene sets, those on the Smokescreen Genotyping Array and the nicotinic acetylcholine receptors, using the largest available GWAS summary statistics. We assessed smoking initiation, cigarettes per day, smoking cessation, and age of smoking initiation.\n\nResultsIndividual genes within each gene set were significantly associated with smoking behaviors. Both sets of genes were significantly associated with cigarettes per day, smoking initiation, and smoking cessation. Age of initiation was only associated with the Smokescreen gene set. While both sets of genes were enriched for trait heritability, each accounts for only a small proportion of the SNP-based heritability (2-12%).\n\nConclusionsThese two gene sets are associated with smoking behaviors, but collectively account for a limited amount of the genetic and phenotypic variation of these complex traits, consistent with high polygenicity.\n\nImplicationsWe evaluated evidence for association and heritable contribution of expert-curated and bioinformatically identified sets of genes related to smoking. Although they impact smoking behaviors, these specifically targeted genes do not account for much of the heritability in smoking and will be of limited use for predictive purposes. Advanced genome-wide approaches and integration of other omics data will be needed to fully account for the genetic variation in smoking phenotypes.

genetics↗

bioRxiv2019

GWAS of Over 427,000 Individuals Establishes GABAergic and Synaptic Molecular Pathways as Key for Cognitive Executive Functions

Deficits in executive functions (EFs), cognitive processes that control goal-directed behaviors, are associated with psychopathology and neurological disorders. Little is known about the molecular bases of EF individual differences; existing EF genome-wide association studies (GWAS) used small sample sizes and/or focused on individual tasks that are imprecise measures of EF. We conducted a GWAS of a Common EF (cEF) factor based on multiple tasks in the UK Biobank (N=427,037 European-descent individuals), finding 129 independent genome-wide significant lead variants in 112 distinct loci. cEF was associated with fast synaptic transmission processes (synaptic, potassium channel, and GABA pathways) in gene-based analyses. cEF was genetically correlated with measures of intelligence (IQ) and cognitive processing speed, but cEF and IQ showed differential genetic associations with psychiatric disorders and educational attainment. Results suggest that cEF is a genetically distinct cognitive construct that is particularly relevant to understanding the genetic variance in psychiatric disorders.

neuroscience↗

bioRxiv2019

Not just one p: Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study (GWAS) of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of mood disturbance (major depressive disorder, bipolar II, and self-reported symptoms of depression, mania, and psychosis) versus rarer forms of serious mental illness (bipolar I, schizoaffective disorder, and schizophrenia). Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind, rather than merely in degree.

genetics↗

bioRxiv2018

Imputation of Behavioral Candidate Gene Repeat Polymorphisms in 486,551 Publicly-Available UK Biobank Individuals.

Some of the most widely studied polymorphisms in psychiatric genetics include variable number tandem repeat polymorphisms (VNTRs) in SLC6A3, DRD4, SLC6A4, and MAOA. While initial findings suggested large effects, their importance with respect to psychiatric phenotypes is the subject of much debate with broadly conflicting results. Despite broad interest, these loci remain absent from the largest available samples, such as the UK Biobank, limiting researchers ability to test these contentious hypotheses rigorously in large samples. Here, using two independent reference datasets, we report out-of-sample imputation accuracy estimates of >0.96 for all four VNTR polymorphisms and one modifying SNP, depending on the reference and target dataset. We describe the imputation procedures of these candidate polymorphisms in 486,551 UK Biobank individuals, and have made the imputed polymorphism data available to UK Biobank researchers. This resource, provided to the community, will allow the most rigorous tests to-date of the roles of these polymorphisms in behavioral and psychiatric phenotypes.

genetics↗

bioRxiv2018

Imprint of Assortative Mating on the Human Genome

Non-random mate-choice with respect to complex traits is widely observed in humans, but whether this reflects true phenotypic assortment, environment (social homogamy) or convergence after choosing a partner is not known. Understanding the causes of mate choice is important, because assortative mating (AM) if based upon heritable traits, has genetic and evolutionary consequences. AM is predicted under Fishers classical theory1 to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify AM on a specific trait by estimating the correlation ({theta}) between genetic predictors of the trait from SNPs on odd versus even chromosomes. We show by theory and simulation that the effect of AM can be distinguished from population stratification. We applied this approach to 32 complex traits and diseases using SNP data from [~]400,000 unrelated individuals of European ancestry. We found significant evidence of AM for height ({theta}=3.2%) and educational attainment ({theta}=2.7%), both consistent with theoretical predictions. Overall, our results imply that AM involves multiple traits, affects the genomic architecture of loci that are associated with these traits and that the consequence of mate choice can be detected from a random sample of genomes.

genetics↗

bioRxiv2018

Relationships between estimated autozygosity and complex traits in the UK Biobank

Inbreeding increases the risk of certain Mendelian disorders in humans but may also reduce fitness through its effects on complex traits and diseases. Such inbreeding depression is thought to occur due to increased homozygosity at causal variants that are recessive with respect to fitness. Until recently it has been difficult to amass large enough sample sizes to investigate the effects of inbreeding depression on complex traits using genome-wide single nucleotide polymorphism (SNP) data in population-based samples. Further, it is difficult to infer causation in analyses that relate degree of inbreeding to complex traits because confounding variables (e.g., education) may influence both the likelihood for parents to outbreed and offspring trait values. The present study used runs of homozygosity in genome-wide SNP data in up to 400,000 individuals in the UK Biobank to estimate the proportion of the autosome that exists in autozygous tracts--stretches of the genome which are identical due to a shared common ancestor. After multiple testing corrections and controlling for possible sociodemographic confounders, we found significant relationships in the predicted direction between estimated autozygosity and three of the 26 traits we investigated: age at first sexual intercourse, fluid intelligence, and forced expiratory volume in 1 second. Our findings for fluid intelligence and forced expiratory volume corroborate those of several published studies while the finding for age at first sexual intercourse was novel. These results may suggest that these traits have been associated with Darwinian fitness over evolutionary time, although there are other possible explanations for these associations that cannot be eliminated. Some of the autozygosity-trait relationships were attenuated after controlling for background sociodemographic characteristics, suggesting that care needs to be taken in the design and interpretation of ROH studies in order to glean reliable information about the genetic architecture and evolutionary history of complex traits.\n\nAuthor SummaryInbreeding is well known to increase the risk of rare, monogenic diseases, and there has been some evidence that it also affects complex traits, such as cognition and educational attainment. However, difficulties can arise when inferring causation in these types of analyses because of the potential for confounding variables (e.g., socioeconomic status) to bias the observed relationships between distant inbreeding and complex traits. In this investigation, we used single-nucleotide polymorphism data in a very large (N > 400,000) sample of seemingly outbred individuals to quantify the degree to which distant inbreeding is associated with 26 complex traits. We found robust evidence that distant inbreeding is inversely associated with fluid intelligence and a measure of lung function, and is positively associated with age at first sex, while other trait associations with inbreeding were attenuated after controlling for background sociodemographic characteristics. Our findings are consistent with evolutionary predictions that fluid intelligence, lung function, and age at first sex have been under selection pressures over time; however, they also suggest that confounding variables must be accounted for in order to reliably interpret results from these types of analyses.

genetics↗

bioRxiv2017

Testing the moderation of quantitative gene by environment interactions in unrelated individuals

The environment can moderate the effect of genes - a phenomenon called gene-environment (GxE) interaction. There are two broad types of GxE modeled in human behavior - qualitative GxE, where the effects of individual genetic variants differ depending on some environmental moderator, and quantitative GxE, where the additive genetic variance changes as a function of an environmental moderator. Tests of both qualitative and quantitative GxE have traditionally relied on comparing the covariances between twins and close relatives, but recently there has been interest in testing such models on unrelated individuals measured on genomewide data. However, to date, there has been no ability to test quantitative GxE effects in unrelated individuals using genomewide data because standard software cannot solve nonlinear constraints. Here, we introduce a maximum likelihood approach with parallel constrained optimization to fit such models. We use simulation to estimate the accuracy, power, and type I error rates of our method and to gauge its computational performance, and then apply this method to IQ data measured on 40,172 individuals with whole-genome SNP data from the UK Biobank. We found that the additive genetic variation of IQ tagged by SNPs increases as socioeconomic status (SES) decreases, opposite the direction found by several twin studies conducted in the U.S. on adolescents, but consistent with several studies from Europe and Australia on adults.

bioinformatics↗