Biology subjects

Smeland, O. B.

Publications and source records attributed to Smeland, O. B..

5 recordsLinked to original sources

bioRxiv2018

Genetic control of variability in subcortical and intracranial volumes

Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in nine key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n=25,575 individuals; 8 to 89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in cortical thickness, thalamus, pallidum, and intracranial volumes. The variance-controlling loci included genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health.

neuroscience↗

bioRxiv2018

The dark side of the mean: brain structural heterogeneity in schizophrenia and its polygenic risk.

ImportanceBetween-subject variability in brain structure is determined by gene-environment interactions, possibly reflecting differential sensitivity to environmental and genetic perturbations. Magnetic resonance imaging (MRI) studies have revealed thinner cortices and smaller subcortical volumes in patients. However, such group-level comparisons may mask considerable within-group heterogeneity, which has largely remained unnoticed in the literature\n\nObjectiveTo compare brain structural variability between individuals with SZ and healthy controls (HC) and to test if respective variability reflects the polygenic risk for SZ (PRS) in HC.\n\nDesign, Setting, and ParticipantsWe compared MRI derived cortical thickness and subcortical volumes between 2,010 healthy controls and 1,151 patients with SZ across 16 cohorts. Secondly, we tested for associations between PRS and MRI features in 12,490 participants from UK Biobank.\n\nMain Outcomes and MeasuresWe modeled mean and dispersion effects of SZ and PRS using double generalized linear models. We performed vertex-wise analyses for thickness, and region-of-interest analysis for cortical, subcortical and hippocampal subfield volumes. Follow-up analyses included within-sample analysis, controlling for intracranial volume and population covariates, test of robustness of PRS threshold, and outlier removal.\n\nResultsCompared to controls, patients with SZ showed higher heterogeneity in cortical thickness, cortical and ventricle volumes, and hippocampal subfields. Higher PRS was associated with thinner frontal and temporal cortices, as well as smaller left CA2/3, but was not significantly associated with dispersion.\n\nConclusion and relevanceSZ is associated with substantial brain structural heterogeneity beyond the mean differences. These findings possibly reflect higher differential sensitivity to environmental and genetic perturbations in patients, supporting the heterogeneous nature of SZ. Higher PRS for SZ was associated with thinner fronto-temporal cortices and smaller subcortical volumes, but there were no significant associations with the heterogeneity in these measures, i.e. the variability among individuals with high PRS were comparable to the variability among individuals with low PRS. This suggests that brain variability in SZ results from interactions between environmental and genetic factors that are not captured by the PGR. Factors contributing to heterogeneity in fronto-temporal cortices and hippocampus are thus key to further our understanding of how genetic and environmental factors shape brain biology in SZ.\n\nKey PointsQuestion: Is schizophrenia and its polygenic risk associated with brain structural heterogeneity in addition to mean changes?\n\nFindings: In a sample of 1151 patients and 2010 controls, schizophrenia was associated with increased heterogeneity in fronto-temporal thickness, cortical, ventricle, and hippocampal volumes, besides robust reductions in mean estimates. In an independent sample of 12,490 controls, polygenic risk for schizophrenia was associated with thinner fronto-temporal cortices and smaller CA2/3 of the left hippocampus, but not with heterogeneity.\n\nMeaning: Schizophrenia is associated with increased inter-individual differences in brainstructure, possibly reflecting clinical heterogeneity, gene-environment interactions, or secondary disease factors.

neuroscience↗

bioRxiv2017

Bivariate Gaussian Mixture Model of GWAS (BGMG)quantifies polygenic overlap between complex traitsbeyond genetic correlation

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. While current cross-trait analytical methods focus on genetic correlation between traits, we developed a novel statistical tool (MiXeR), which quantifies polygenic overlap independent of genetic correlation, using summary statistics from GWAS. MiXeR results can be presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that more than 9K variants causally influence schizophrenia, 7K influence bipolar disorder, and out of those variants 6.9K are shared between these two disorders, which have high genetic correlation. Further, MiXeR uncovers extensive polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, these traits share more than 9K causal variants, while 3K additional variants only influence educational attainment. By considering the polygenicity, heritability and discoverability of complex phenotypes, MiXeR provides a more complete quantification of shared genetic architecture than offered by other available tools.

genetics↗

bioRxiv2017

Estimating inflation in GWAS summary statistics due to variance distortion from cryptic relatedness

Cryptic relatedness is inherently a feature of large genome-wide association studies (GWAS), and can give rise to considerable inflation in summary statistics for single nucleotide polymorphism (SNP) associations with phenotypes. It has proven difficult to disentangle these inflationary effects from true polygenic effects. Here we present results of a model that enables estimation of polygenicity, mean strength of association, and residual inflation in GWAS summary statistics. We show that there is substantial residual inflation in recent large GWAS of height and schizophrenia; correcting for this reduces the number of independent genome-wide significant loci from the reported values of 697 for height and 108 for schizophrenia to 368 and 61, respectively. In contrast, a larger GWAS of educational attainment shows no residual inflation. Additionally, we find that height has a relatively low polygenicity, with approximately 8k SNPs having causal association, more than an order of magnitude less than has been reported. The residual inflation in GWAS summary statistics can be corrected using the standard genomic control procedure with the estimated residual inflation factor.

genomics↗

bioRxiv2017

Estimating Degree Of Polygenicity, Causal Effect Size Variance, And Confounding Bias In GWAS Summary Statistics

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from [~=] 2 x 10-5 to [~=] 4 x 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics. Author SummaryThere are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype. Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants - among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants - we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities - whose product allows for lower bound estimates of heritability - vary by orders of magnitude.

genomics↗