Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits.

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from 5 ancestry groups. In the combined meta-analyses of Stages 1 and 2, we identified 59 loci (p value <5e-8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A, PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5, CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations.

Model-based linkage analysis with imprinting for quantitative traits: ignoring imprinting effects can severely jeopardize detection of linkage.

Genes with imprinting (parent-of-origin) effects express differently when inheriting from the mother or from the father. Some genes for development and behavior in mammals are known to be imprinted. We developed parametric linkage analysis that accounts for imprinting effects for continuous traits, implementing it in MORGAN. To study misspecification of imprinting on linkage analysis, we simulated eight markers over a 35 cM region with phenotypes where imprinting contributes 0, 25, 50, and 75% of the variance of a quantitative trait locus (QTL) effect and analyzed them under all four models. Multipoint lod scores were computed and maximized over the same 35 cM region. Our most important finding is the dramatic lod score improvement under the correct imprinting model over the no-imprinting model. For data with minor QTL allele frequency 0.05, the correct model provided the highest lod scores with maximum expected lod scores over 4 in all settings. Ignoring imprinting provided the lowest lod scores with maximum expected lod scores between -9.9 and 2.4. In the extreme scenario, cases with max lod > or =3 from the correct imprinting model and max lod < or =-2 from the no-imprinting model occurred in 86% of replications. Models with misspecified imprinting produced lod scores intermediate between those with correct imprinting and with no imprinting. The effects of model misspecification were less pronounced for singlepoint analysis. Our multipoint results illustrate that ignoring true imprinting severely impairs detection of linkage and erroneously excludes genomic regions (with max lod <-2), whereas accounting for it can substantially improve linkage detection.

MCMC-based linkage analysis for complex traits on general pedigrees: multipoint analysis with a two-locus model and a polygenic component.

We describe a new program lm_twoqtl, part of the MORGAN package, for parametric linkage analysis with a quantitative trait locus (QTL) model having one or two QTLs and a polygenic component, which models additional familial correlation from other unlinked QTLs. The program has no restriction on number of markers or complexity of pedigrees, facilitating use of more complex models with general pedigrees. This is the first available program that can handle a model with both two QTLs and a polygenic component. Competing programs use only simpler models: one QTL, one QTL plus a polygenic component, or variance components (VC). Use of simple models when they are incorrect, as for complex traits that are influenced by multiple genes, can bias estimates of QTL location or reduce power to detect linkage. We compute the likelihood with Markov Chain Monte Carlo (MCMC) realization of segregation indicators at the hypothesized QTL locations conditional on marker data, summation over phased multilocus genotypes of founders, and peeling of the polygenic component. Simulated examples, with various sized pedigrees, show that two-QTL analysis correctly identifies the location of both QTLs, even when they are closely linked, whereas other analyses, including the VC approach, fail to identify the location of QTLs with modest contribution. Our examples illustrate the advantage of parametric linkage analysis with two QTLs, which provides higher power for linkage detection and better localization than use of simpler models.