Biology subjects

Kraetschmer, I.

Publications and source records attributed to Kraetschmer, I..

3 recordsLinked to original sources

bioRxiv2025

Separating direct, indirect and parent-of-origin genetic effects in the human population

Here, we present a novel approach to estimate the degree to which the phenotypic effect of a DNA locus is attributable to four components: alleles in the child (direct genetic effects), alleles in the mother and the father (indirect genetic effects), or is dependent upon the parent from which it is inherited (parent-of-origin, PofO effects). Applying our model, JODIE, to 30,000 child-mother-father trios with phased DNA information from the Estonian Biobank (EstBB) and the Norwegian Mother, Father, Child Cohort (MoBa), we jointly estimate the phenotypic variance attributable to these four effects unbiased of assortative mating (AM) for height, body mass index (BMI) and childhood educational test score (EA). For all three traits, direct effects make the largest contribution to the genetic effect variance. But we find that parental indirect genetic effects make an equivalent combined contribution, and that there is a non-zero PofO effect variance for all traits. We calculate the heritability that would be obtained at the populationlevel in the absence of AM for common DNA loci, and show that the proportional contribution of direct effects to these heritability values can be calculated as 64.0% for EA in MoBa, 77.1% and 63.4% for height in MoBa and EstBB, and 81.2% and 88.0% for BMI in MoBa and EstBB. Additionally, using within-family genome-wide association testing, we identify 276 independently associated DNA regions that replicate across two additional biobanks, which all show a genotype-phenotype relationship that reflects an interplay of direct, indirect and PofO effects. Determining how direct, parental and PofO genetic effects combine across loci genome-wide to influence human phenotypic variation requires joint modeling of parental and child genotypes alongside the parental origin of loci and here, we make the first attempt to do this in the human population.

genetics↗

bioRxiv2024

Confounding of indirect genetic and epigenetic effects

An individuals phenotype reflects a complex interplay of the direct effects of their DNA, epigenetic modifications of their DNA induced by their parents, and indirect effects of their parents DNA. Here, we derive how the genetic variance within a population is changed under the influence of indirect maternal, paternal and parent-of-origin effects under random mating. We also consider indirect effects of a sibling, in particular how the genetic variance is altered when looking at the phenotypic difference between two siblings. The calculations are then extended to include assortative mating (AM), which alters the variance by inducing increased homozygosity and correlations within and across loci. AM likely leads to covariance of parental genetic effects, a measure of the similarity of parents in the indirect effects they have on their children. We propose that this assortment for parental characteristics, where biological parents create similar environments for their children, can create shared parental effects across traits and the appearance of cross-trait AM. Interestingly, the genetic variances is increased under AM for the childmother-father design, while it is decreased for the sibling difference. Our results demonstrate that it is not possible to get unbiased estimates of direct genetic effects without controlling for parental and parent-of-origin effects.

genetics↗

bioRxiv2023

Causal inference for multiple risk factors and diseases from genomics data

Statistical causal learning in genome-wide association studies (GWAS) relies on the instrumental variable method of Mendelian Randomization (MR). Currently, an over-whelming number of MR studies purport to show causal relationships among a wide range of risk factors and outcomes. Here, we find that naive application of many recently proposed MR approaches results in numerous null relationships being discovered as highly significant. We show that a well-controlled error rate can be achieved through a graphical inference approach which: (i) selects a set of genetic instrumental variables (IVs) from GWAS summary static controlling for LD, linkage and pleiotropy; (ii) accommodates rare variants and binary outcomes in a principled way; (iii) distinguishes direct from indirect risk factors in very high-dimensional data; and (iv) identifies potential unobserved latent confounding. Only 20 minutes of wall-clock compute time is required for our Causal Inference GWAS (CI-GWAS) approach to jointly analyze a set of 9 common health risk factors, four common complex metabolic disease outcomes and 8.4M genetic variants recorded for 458,747 individuals in the UK Biobank. Genome-wide, we find that very few genetic variants are suitable MR IVs, with only 696 variants remaining when analyzing all traits jointly. While we replicate almost all paths previously found by CAUSE between risk factors and outcomes, we show that only few of these reflect direct adjacencies and that many cannot be distinguished from unmeasured confounding within the UK Biobank data. Our results suggest that well-curated longitudinal records and family data are likely needed to overcome the mixtures of temporal precedence and reverse-causality in biobank data. Our approach provides a first-step toward robust principled screening for potential causal links to understand the underlying nature of phenotypic correlations in biobank data.

genetics↗