Biological Measures in the WLS: Genetic and Microbiome Data

Abstract Ever since releasing genotype data in 2017, the WLS continually expands resources available to users interested in genetic research. Key advantages to the WLS data for genetics research include its sibling sample and nearly full life course longitudinal study design. In 2021, we now have state-of-the-art polygenic scores available in multiple domains, such as health, cognition, fertility, personality, risk behaviors and attitudes, and life satisfaction. The scores cover phenotypes spanning from adventurousness, through educational attainment, to age at which voice deepened. Additionally, the genotype data was re-imputed in 2021 to the superior Haplotype Reference Consortium reference panel and the WLS expects to obtain copy number variants data next year. In addition to genetic data, we have a set of novel microbiome data on a subset of participants that allows researchers to study relationships between environments and gut microbial composition.

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Polygenic burden has broader impact on health, cognition, and socioeconomic outcomes than most rare and high-risk copy number variants

Molecular Psychiatry ◽

10.1038/s41380-021-01026-z ◽

2021 ◽

Author(s):

Elmo Christian Saarentaus ◽

Aki Samuli Havulinna ◽

Nina Mars ◽

Ari Ahola-Olli ◽

Tuomo Tapio Johannes Kiiskinen ◽

...

Keyword(s):

High Risk ◽

Educational Attainment ◽

Household Income ◽

Copy Number ◽

Copy Number Variants ◽

Well Being ◽

Subjective Health ◽

Polygenic Risk Score ◽

The Impact ◽

Health Cognition

AbstractCopy number variants (CNVs) are associated with syndromic and severe neurological and psychiatric disorders (SNPDs), such as intellectual disability, epilepsy, schizophrenia, and bipolar disorder. Although considered high-impact, CNVs are also observed in the general population. This presents a diagnostic challenge in evaluating their clinical significance. To estimate the phenotypic differences between CNV carriers and non-carriers regarding general health and well-being, we compared the impact of SNPD-associated CNVs on health, cognition, and socioeconomic phenotypes to the impact of three genome-wide polygenic risk score (PRS) in two Finnish cohorts (FINRISK, n = 23,053 and NFBC1966, n = 4895). The focus was on CNV carriers and PRS extremes who do not have an SNPD diagnosis. We identified high-risk CNVs (DECIPHER CNVs, risk gene deletions, or large [>1 Mb] CNVs) in 744 study participants (2.66%), 36 (4.8%) of whom had a diagnosed SNPD. In the remaining 708 unaffected carriers, we observed lower educational attainment (EA; OR = 0.77 [95% CI 0.66–0.89]) and lower household income (OR = 0.77 [0.66–0.89]). Income-associated CNVs also lowered household income (OR = 0.50 [0.38–0.66]), and CNVs with medical consequences lowered subjective health (OR = 0.48 [0.32–0.72]). The impact of PRSs was broader. At the lowest extreme of PRS for EA, we observed lower EA (OR = 0.31 [0.26–0.37]), lower-income (OR = 0.66 [0.57–0.77]), lower subjective health (OR = 0.72 [0.61–0.83]), and increased mortality (Cox’s HR = 1.55 [1.21–1.98]). PRS for intelligence had a similar impact, whereas PRS for schizophrenia did not affect these traits. We conclude that the majority of working-age individuals carrying high-risk CNVs without SNPD diagnosis have a modest impact on morbidity and mortality, as well as the limited impact on income and educational attainment, compared to individuals at the extreme end of common genetic variation. Our findings highlight that the contribution of traditional high-risk variants such as CNVs should be analyzed in a broader genetic context, rather than evaluated in isolation.

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Bioconductor workflow for microbiome data analysis: from raw reads to community analyses

F1000Research ◽

10.12688/f1000research.8986.1 ◽

2016 ◽

Vol 5 ◽

pp. 1492 ◽

Cited By ~ 148

Author(s):

Ben J. Callahan ◽

Kris Sankaran ◽

Julia A. Fukuyama ◽

Paul J. McMurdie ◽

Susan P. Holmes

Keyword(s):

High Throughput Sequencing ◽

Linear Models ◽

Reference Database ◽

Rrna Gene ◽

Microbial Composition ◽

Nonparametric Testing ◽

Abundance Estimates ◽

Taxonomic Markers ◽

Microbiome Data ◽

Microbiome Data Analysis

High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to quantify and compare abundance levels or microbial composition of communities in different conditions. The sequencing reads have to be denoised and assigned to the closest taxa from a reference database. Common approaches use a notion of 97% similarity and normalize the data by subsampling to equalize library sizes. In this paper, we show that statistical models allow more accurate abundance estimates. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, including both parameteric and nonparametric methods. We provide examples of using the R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests, partial least squares and linear models as well as nonparametric testing using community networks and the ggnetwork package.

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Insights into Dyslexia Genetics Research from the Last Two Decades

Brain Sciences ◽

10.3390/brainsci12010027 ◽

2021 ◽

Vol 12 (1) ◽

pp. 27

Author(s):

Florina Erbeli ◽

Marianne Rice ◽

Silvia Paracchini

Keyword(s):

Language Disorder ◽

Association Studies ◽

Neurodevelopmental Disorder ◽

Molecular Genetic ◽

Genetic Research ◽

Twin Studies ◽

Genome Wide Association Studies ◽

Genetics Research ◽

Genome Wide ◽

Genetic Risks

Dyslexia, a specific reading disability, is a common (up to 10% of children) and highly heritable (~70%) neurodevelopmental disorder. Behavioral and molecular genetic approaches are aimed towards dissecting its significant genetic component. In the proposed review, we will summarize advances in twin and molecular genetic research from the past 20 years. First, we will briefly outline the clinical and educational presentation and epidemiology of dyslexia. Next, we will summarize results from twin studies, followed by molecular genetic research (e.g., genome-wide association studies (GWASs)). In particular, we will highlight converging key insights from genetic research. (1) Dyslexia is a highly polygenic neurodevelopmental disorder with a complex genetic architecture. (2) Dyslexia categories share a large proportion of genetics with continuously distributed measures of reading skills, with shared genetic risks also seen across development. (3) Dyslexia genetic risks are shared with those implicated in many other neurodevelopmental disorders (e.g., developmental language disorder and dyscalculia). Finally, we will discuss the implications and future directions. As the diversity of genetic studies continues to increase through international collaborate efforts, we will highlight the challenges in advances of genetics discoveries in this field.

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Atlas of functional connectivity relationships across rare and common genetic variants, traits, and psychiatric conditions

10.1101/2021.05.21.21257604 ◽

2021 ◽

Author(s):

Clara A Moreau ◽

Kuldeep Kumar ◽

Annabelle Harvey ◽

Guillaume Huguet ◽

Sebastian Urchs ◽

...

Keyword(s):

Psychiatric Disorders ◽

Large Scale ◽

Brain Connectivity ◽

Copy Number Variants ◽

Effect Sizes ◽

Genomic Variants ◽

Common Genetic Variants ◽

Polygenic Scores ◽

Genetic Risks ◽

Psychiatric Conditions

Polygenicity and pleiotropy are key properties of the genomic architecture of psychiatric disorders. An optimistic interpretation of polygenicity is that genomic variants converge on a limited set of mechanisms at some level from genes to behavior. Alternatively, convergence may be minimal or absent. We took advantage of brain connectivity, measured by resting-state functional MRI (rs-fMRI), as well as rare and common genomic variants to understand the effects of polygenicity and pleiotropy on large-scale brain networks, a distal step from genes to behavior. We processed ten rs-fMRI datasets including 32,988 individuals, to examine connectome-wide effects of 16 copy number variants (CNVs), 10 polygenic scores, 6 cognitive and brain morphometry traits, and 4 idiopathic psychiatric conditions. Although effect sizes of CNVs on connectivity were correlated to cognition and number of genes, increasing polygenicity was associated with decreasing effect sizes on connectivity. Accordingly, the effect sizes of polygenic scores on connectivity were 6-fold lower compared to CNVs. Despite this heterogeneity of connectivity profiles, multivariate analysis identified convergence of genetic risks and psychiatric disorders on the thalamus and the somatomotor network. Based on spatial correlations with transcriptomic data, we hypothesize that excitatory thalamic neurons may be primary contributors to brain alteration profiles shared across genetic risks and conditions. Finally, pleiotropy measured by genetic and transcriptomic correlations between 38 pairs of conditions/traits showed significant concordance with connectomic correlations, suggesting a substantial causal genetic component for shared connectivity. Such findings open avenues to delineate general mechanisms - amenable to intervention - across conditions and genetic risks.

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A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008473 ◽

2020 ◽

Vol 16 (12) ◽

pp. e1008473

Author(s):

Pamela N. Luna ◽

Jonathan M. Mansbach ◽

Chad A. Shaw

Keyword(s):

Joint Modeling ◽

Mixed Effects ◽

Mixed Effects Model ◽

Time Dependent ◽

Time To Event ◽

Modeling Framework ◽

Microbial Composition ◽

Disease Outcomes ◽

Microbiome Data ◽

Over Time

Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.

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Teaching the Teachers the Genetics of Learning: An Application of the Mixed Blessings Model

10.31234/osf.io/gwdtm ◽

2021 ◽

Author(s):

Sally A. Larsen ◽

Callie Little

Keyword(s):

Genetic Variation ◽

Teacher Perceptions ◽

Genetic Information ◽

Teaching Practice ◽

Genetic Research ◽

Genetic Influences ◽

School Contexts ◽

Genetics Research ◽

New Directions ◽

And Behavior

Decades of educational genetics research has highlighted that differences in academic achievement are partly explained by genetic variation between individuals. Consequently, there is ongoing discussion about whether genetic influences on educationally-related traits should be more widely acknowledged in schools and communicated specifically to teachers. Nonetheless, there is little research on how teachers might interpret such information, and how it might alter their perceptions of the students they teach, or their teaching practice. In this review we draw on the mixed blessings model proposed by Haslam and Kvaale (2015) as a framework for defining both positive and negative repercussions of disseminating the findings of educational genetic research to teachers. We discuss research examining teacher perceptions of student ability and behavior, and findings outlining perceptions of psychological disorders when genetic explanations are invoked. We conclude by proposing new directions for research designed to better understand interpretations of genetic information in school contexts.

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Sociology, Genetics, and the Coming of Age of Sociogenomics

Annual Review of Sociology ◽

10.1146/annurev-soc-121919-054756 ◽

2020 ◽

Vol 46 (1) ◽

pp. 553-581 ◽

Cited By ~ 1

Author(s):

Melinda C. Mills ◽

Felix C. Tropf

Keyword(s):

Coming Of Age ◽

Association Studies ◽

Molecular Genetic ◽

Genetic Correlations ◽

Genetic Research ◽

Well Being ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Molecular Genetic Data ◽

Polygenic Scores

Recent years have seen the birth of sociogenomics via the infusion of molecular genetic data. We chronicle the history of genetics, focusing particularly on post-2005 genome-wide association studies, the post-2015 big data era, and the emergence of polygenic scores. We argue that understanding polygenic scores, including their genetic correlations with each other, causation, and underlying biological architecture, is vital. We show how genetics can be introduced to understand a myriad of topics such as fertility, educational attainment, intergenerational social mobility, well-being, addiction, risky behavior, and longevity. Although models of gene-environment interaction and correlation mirror agency and structure models in sociology, genetics is yet to be fully discovered by this discipline. We conclude with a critical reflection on the lack of diversity, nonrepresentative samples, precision policy applications, ethics, and genetic determinism. We argue that sociogenomics can speak to long-standing sociological questions and that sociologists can offer innovative theoretical, measurement, and methodological innovations to genetic research.

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Understanding Microbiome Data: A Primer for Clinicians

Digestive Diseases ◽

10.1159/000437034 ◽

2015 ◽

Vol 33 (Suppl. 1) ◽

pp. 11-16 ◽

Cited By ~ 13

Author(s):

Philippe Seksik ◽

Cécilia Landman

Keyword(s):

Gut Microbiota ◽

Bacterial Colonization ◽

Data Sets ◽

Human Gut ◽

Microbial Composition ◽

Host Determinants ◽

Host Interaction ◽

Bacterial Genes ◽

Micro Organisms ◽

Microbiome Data

The human gut contains 1014 bacteria and many other micro-organisms such as Archaea, viruses and fungi. This gut microbiota has co-evolved with host determinants through symbiotic and co-dependent relationships. Bacteria, which represent 10 times the number of human cells, form the most depicted part of this black box owing to new tools. Re-evaluating the gut microbiota showed how this entity participates in gut physiology and beyond this in human health. Studying and handling this real ‘hidden organ' remains a challenge for clinicians. In this review, we aimed to bring information about gut microbiota, its structure, its roles and the way to capture and measure it. After bacterial colonization in infant, intestinal microbial composition is unique for each individual although more than 95% can be assigned to 4 major phyla. Besides its biodiversity, the major characteristics of gut microbiota are stability over time and resilience after perturbation. In pathological situations, dysbiosis (i.e. imbalance in gut microbiota composition) is observed with a loss in overall diversity. Dysbiosis associated with inflammatory bowel disease was specified with the reduction in biodiversity, the decreased representation of different taxa in the Firmicutes phylum and an increase in Gammaproteobacteria. Beyond depicting gut microbial composition, metagenomics allows the description of the combined genomes of the microorganisms present in the gut, giving access to their potential functions. In fact, each individual overall microbial metagenome outnumbers the size of human genome by a factor of 150. Besides a functional core in which there is redundancy for mandatory functions assuring the robustness of the ecosystem, human gut contains an important diversity and high number of non-redundant bacterial genes. Clinical data, treatment and all the factors able to influence microbiome should enter integrated big data sets to put in light pathways of interplay within the supra organism composed of gut microbiome and host. A better understanding of dynamics within human gut microbiota and microbes-host interaction will allow new insight into gut pathophysiology especially regarding resilience mechanisms and dysbiosis onset and maintenance. This will lead to description of biomarkers of diseases, development of new probiotics/prebiotics and new therapies.

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Clinical autism subscales have common genetic liability that is heritable, pleiotropic, and generalizable to the general population

10.1101/2021.08.30.21262845 ◽

2021 ◽

Author(s):

Taylor R Thomas ◽

Tanner Koomar ◽

Lucas Casten ◽

Ashton Tener ◽

Ethan Bahl ◽

...

Keyword(s):

General Population ◽

Developmental Coordination Disorder ◽

Genetic Correlations ◽

Genetic Research ◽

Case Control ◽

Repetitive Behaviors ◽

Phenotypic Correlation ◽

Polygenic Scores ◽

Negative Effect ◽

Genetic Signal

The complexity of autism's phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is unclear whether clinical autism instruments such as the Social Communication Questionnaire (SCQ), Repetitive Behaviors Scale-Revised (RBS-R), and Developmental Coordination Disorder Questionnaire (DCDQ) are more genetically informative than case-control. We employed the SPARK autism cohort (N = 6,449) to illuminate the genetic etiology of these twelve subscales. In comparison to the heritability of autism case-control at 0.12, the RBS-R subscales were increased, ranging from 0.18 to 0.30 (all p < 0.05). Heritability of the DCDQ subscales ranged from 0.07 to 0.09 and the SCQ subscales from 0 to 0.09 (all p > 0.05). We also found evidence for genetic correlations among the RBS-R, SCQ, and DCDQ. GWAS followed by projection of polygenic scores (PGS) into ABCD revealed significant associations with CBCL social and thought problems, while the autism case-control PGS did not significantly associate. In phenotypic correlation analyses, the autism case-control PGS did not predict the subscales in SPARK, and sex-stratified correlations showed no effect in males and a surprising negative effect in females. Notably, other PGS did predict the subscales, with the strongest being educational attainment negatively correlated, while ADHD and major depression were positively correlated. Overall, our analyses suggest that clinical subscales are more genetically powerful than case-control, and that of the three instruments investigated, the RBS-R shows the greatest evidence of common genetic signal in both autistic and general population samples.

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A Latent Allocation Model for the Analysis of Microbial Composition and Disease

10.1101/396903 ◽

2018 ◽

Author(s):

Ko Abe ◽

Masaaki Hirayama ◽

Kinji Ohno ◽

Teppei Shimamura

Keyword(s):

Multinomial Distribution ◽

Real Data ◽

Case Control Studies ◽

Probability Models ◽

Microbial Composition ◽

Allocation Model ◽

Number Of Zeros ◽

Learning Procedure ◽

Efficient Learning ◽

Microbiome Data

AbstractBackgroundEstablishing the relationship between microbiota and specific disease is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data.ResultsThe purpose of this article is to develop a new probabilistic model, called BERMUDA (BERnoulli and MUltinomial Distribution-based latent Allocation), to address these problems. BERMUDA enables us to describe the differences in bacteria composition and a certain disease among samples. We also provide a simple and efficient learning procedure for the proposed model using an annealing EM algorithm.ConclusionWe illustrate the performance of the proposed method both through both the simulation and real data analysis. BERMUDA is implemented with R and is available from GitHub (https://github.com/abikoushi/Bermuda).

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