Presence Of Genetic Correlations Research Articles

Shared genetic background between SARS-CoV-2 infection and large artery stroke.

Increased risk of stroke, particularly large artery stroke (LAS), has been observed in patients with COVID-19. The biological processes underlying the observed higher risk are still unknown. We explored the association between stroke subtypes and COVID-19 susceptibility to understand whether biological mechanisms specific to SARS-CoV-2 uptake/infection could be leading to excess stroke risk in this population. We constructed a polygenic risk score (PRS) of COVID-19 susceptibility and tested its association with stroke subtypes using individual- and summary-level genetic data (SiGN, MEGASTROKE). We generated co-expression networks of genes involved in SARS-CoV-2 uptake/infection (ACE2, TMPRSS2, BEST3, ISLR2 and ADAM17) based on existing tissue expression libraries. Gene-based association testing was performed using S-PrediXcan and VEGAS2. Permutation independence tests were performed to assess SARS-CoV-2-related gene enrichment in stroke and its subtypes. Our PRS demonstrated an association between COVID-19 susceptibility and LAS in SiGN (OR = 1.05 per SD increase, 95% CI: (1.00, 1.10), p = 0.04) and MEGASTROKE (β = 0.510, 95% CI: (0.242, 0.779), FDR-p = 0.0019). The SARS-CoV-2 risk-related ISLR2 co-expression gene network was significantly associated with genetic risk of LAS in aorta, tibial arteries, and multiple brain regions (P < 0.05). Presence of genetic correlation and significant pathway enrichment suggest that increases in LAS risk reported in COVID-19 patients may be intrinsic to the viral infection, rather than a more generalized response to severe illness.

A robust method for collider bias correction in conditional genome-wide association studies

Estimated genetic associations with prognosis, or conditional on a phenotype (e.g. disease incidence), may be affected by collider bias, whereby conditioning on the phenotype induces associations between causes of the phenotype and prognosis. We propose a method, ‘Slope-Hunter’, that uses model-based clustering to identify and utilise the class of variants only affecting the phenotype to estimate the adjustment factor, assuming this class explains more variation in the phenotype than any other variant classes. Simulation studies show that our approach eliminates the bias and outperforms alternatives even in the presence of genetic correlation. In a study of fasting blood insulin levels (FI) conditional on body mass index, we eliminate paradoxical associations of the underweight loci: COBLLI; PPARG with increased FI, and reveal an association for the locus rs1421085 (FTO). In an analysis of a case-only study for breast cancer mortality, a single region remains associated with more pronounced results.

SU22 - BIVARIATE GAUSSIAN MIXTURE MODEL FOR GWAS SUMMARY STATISTICS

Background Identifying shared genetics is important as it uncovers hidden relationship between complex traits and improves our understanding of disease etiology. Today genetic correlation is commonly used as the principal measure that quantifies genetic overlap. Available methods can calculate genetic correlation from raw genotypes (restricted maximum likelihood, polygenic risk scores), from a set of Single-Nucleotide Polymorphisms (SNPs) that pass genome-wide significance threshold (Mendelian Randomization), or utilizing all data from Genome Wide Association Studies (GWASes) including SNPs that do not reach genome-wide significance (Cross-Trait Linkage Disequilibrium (LD) Score Regression). These methods for evaluating genetic overlap are unable to capture a mixture of effect directions across shared genetic variants (i.e., only reporting overall positive, negative or no genetic correlations), while recent analyses suggest that correlation across polygenic traits in the effect size and directionality of a SNP is not the same across all SNPs. Methods Bivariate Gaussian Mixture Model provides a novel way to detect and quantify shared genetics independently from genetic correlation. In the absence of genetic correlation we use another measure of polygenic overlap, expressed as the proportion of SNPs associated with both traits. To estimate this quantity, we model true per-SNP effect sizes as a mixture of four bivariate normal distributions: two causal components specific to each trait, one causal component of SNPs affecting both traits, and a null component of SNPs with no effect on either trait. We interpret the weight of each component in the mixture as the proportion of SNPs in that component. The parameters of the mixture model are estimated from the summary statistics data by direct optimization of the maximum likelihood. Our statistical model relates observed signed test statistics (GWAS z-scores) to the underlying per-SNP effect sizes, incorporating effects of LD structure, minor allele frequency, sample size, cryptic relationships / sample stratification, and sample overlap, to capture all these effects on GWAS z-scores. Results We show in simulations that our model differentiates cases with no polygenic overlap versus cases with significant overlap in the absence of genetic correlation. We show genetic overlap between schizophrenia, waist-hip-ratio and triglycerides, despite the fact that genetic correlation is close to zero. We apply our model to schizophrenia, bipolar disorder and IQ data, and show that in the presence of genetic correlation our model reports consistent results with those from cross-trait LD score regression. Discussion It appears to be a common practice to interpret the lack of genetic correlation as no relation between traits. However, lack of correlation does not necessarily imply independence - a fact well known from statistics. Thus, we advocate that certain important cases of genetic overlap are not captured by genetic correlation. Our model quantifies genetic overlap between traits both with and without genetic correlation. It controls for the fact that if both traits are highly polygenic then some proportion of SNPs is associated with both traits by chance or due to LD structure. In addition to the insights into genetic architecture our model can improve SNP discovery, by estimating posterior effect size of one trait given observed GWAS z-score in another trait. Further, more accurately estimated effect sizes can be used for improving polygenic risk scores.

Genome‐wide association analysis for feed efficiency in Angus cattle

Estimated breeding values for average daily feed intake (AFI; kg/day), residual feed intake (RFI; kg/day) and average daily gain (ADG; kg/day) were generated using a mixed linear model incorporating genomic relationships for 698 Angus steers genotyped with the Illumina BovineSNP50 assay. Association analyses of estimated breeding values (EBVs) were performed for 41 028 single nucleotide polymorphisms (SNPs), and permutation analysis was used to empirically establish the genome-wide significance threshold (P < 0.05) for each trait. SNPs significantly associated with each trait were used in a forward selection algorithm to identify genomic regions putatively harbouring genes with effects on each trait. A total of 53, 66 and 68 SNPs explained 54.12% (24.10%), 62.69% (29.85%) and 55.13% (26.54%) of the additive genetic variation (when accounting for the genomic relationships) in steer breeding values for AFI, RFI and ADG, respectively, within this population. Evaluation by pathway analysis revealed that many of these SNPs are in genomic regions that harbour genes with metabolic functions. The presence of genetic correlations between traits resulted in 13.2% of SNPs selected for AFI and 4.5% of SNPs selected for RFI also being selected for ADG in the analysis of breeding values. While our study identifies panels of SNPs significant for efficiency traits in our population, validation of all SNPs in independent populations will be necessary before commercialization.

VARIANCES AND COVARIANCES OF PHENOLOGICAL TRAITS IN A WILD MAMMAL POPULATION

In a seasonal environment, there are multiple aspects of timing, or phenology, that contribute to an individual's fitness. Several studies have shown a genetic basis to variation between individuals in breeding time, but we know little about the heritability of other phenological traits in wild populations. Furthermore, the presence of genetic correlations between phenological variables could act to constrain or promote any response to selection, but less is known of the multivariate genetic relationships underlying phenological traits in the wild. Here, we use data from a wild population of red deer on the Isle of Rum, Scotland, to investigate covariances between eight phenological traits. Variation was characterized at the level of the phenotype, genotype, and year, and traits measured in different sexes enabled us to test for cross-sex genetic correlations. Phenotypic correlations were broadly strong and positive, as were correlations between traits expressed in the same year. We found evidence of significant additive genetic variation in five of the eight phenological traits studied. However there was little evidence of genetic correlations between traits, implying that much of the observed phenotypic correlation was environmentally induced. Our results suggest that different phenological traits may be free to move along independent evolutionary trajectories.

Electrophysiological indicators as endophenotypes of cognitive development

Parameters of the components of visual evoked potentials recorded in children aged 8 and 11 years make it possible to predict intelligence indices (according to Wechsler’s test) of the same individuals at the age of 22–23 years. The efficiency of this prediction exhibits dependence on the parameter of the evoked potential (EP), the type of the stimulus, site of recording, and the subject’s age. The presence of genetic correlations between the EP parameters and intelligence indices indicates the existence of common genetic factors involved in the formation of inter-age, inter-level phenotypic relationships of different indices of cognitive functions of a person.

Ecological and Evolutionary Consequences of Multispecies Plant-Animal Interactions

▪ Abstract Ecologists and evolutionary biologists are broadly interested in how the interactions among organisms influence their abundance, distribution, phenotypes, and genotypic composition. Recently, we have seen a growing appreciation of how multispecies interactions can act synergistically or antagonistically to alter the ecological and evolutionary outcomes of interactions in ways that differ fundamentally from outcomes predicted by pairwise interactions. Here, we review the evidence for criteria identified to detect community-based, diffuse coevolution. These criteria include (a) the presence of genetic correlations between traits involved in multiple interactions, (b) interactions with one species that alter the likelihood or intensity of interactions with other species, and (c) nonadditive combined effects of multiple interactors. In addition, we review the evidence that multispecies interactions have demographic consequences for populations, as well as evolutionary consequences. Finally, we explore the experimental and analytical techniques, and their limitations, used in the study of multispecies interactions. Throughout, we discuss areas in particular need of future research.