Contribution of Inbred Singletons to Variance Component Estimation of Heritability and Linkage

Objectives: An interesting consequence of consanguinity is that the inbred singleton becomes informative for genetic variance. We determine the contribution of an inbred singleton to variance component analysis of heritability and linkage. Methods: Statistical theory for the power of variance component analysis of quantitative traits is used to determine the expected contribution of an inbred singleton to likelihood-ratio tests of heritability and linkage. Results: In variance component models an inbred singleton contributes relatively little to a test of heritability, but can contribute substantively to a test of linkage. For small to moderate QTL effects and a level of inbreeding comparable to matings between first cousins (the preferred form of union in many human populations), an inbred singleton can carry nearly 25% the information of a non-inbred sibpair. In more highly inbred contexts available with experimental animal populations, nonhuman primate colonies, and some human subpopulations, the contribution of an inbred singleton relative to a sibpair can exceed 50%. Conclusions: Inbred individuals, even in isolation from other members of a sample, can contribute to variance component estimation and tests of heritability and linkage. Under certain conditions the informativeness of the inbred singleton can approach that of non-inbred sibpair

Novel Genetic Loci Underlying Human Intracranial Volume Identified through Genome-Wide Association

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five novel loci for intracranial volume and confirmed two known signals. Four of the loci are also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic=0.748), which indicated a similar genetic background and allowed for the identification of four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, Parkinson’s disease, and enriched near genes involved in growth pathways including PI3K–AKT signaling. These findings identify biological underpinnings of intracranial volume and provide genetic support for theories on brain reserve and brain overgrowth

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

Identification of genes associated with brain aging should markedly improve our understanding of the biological processes that govern normal age-related decline. However, challenges to identifying genes that facilitate successful brain aging are considerable, including a lack of established phenotypes and difficulties in modeling the effects of aging per se, rather than genes that influence the underlying trait. In a large cohort of randomly selected pedigrees (n = 1,129 subjects), we documented profound aging effects from young adulthood to old age (18-83 y) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for pleiotropy between these classes of phenotypes was observed. Applying an advanced quantitative gene-by-environment interaction analysis where age is treated as an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration as a function of age. Furthermore, by decomposing gene-by-aging (G × A) interactions, we infer that different genes influence some neurocognitive traits as a function of age, whereas other neurocognitive traits are influenced by the same genes, but to differential levels, from young adulthood to old age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. These results clearly demonstrate that traits sensitive to the genetic influences on brain aging can be identified, a critical first step in delineating the biological mechanisms of successful aging

Genome sequencing unveils a regulatory landscape of platelet reactivity

Platelet aggregation at the site of atherosclerotic vascular injury is the underlying pathophysiology of myocardial infarction and stroke. To build upon prior GWAS, here we report on 16 loci identified through a whole genome sequencing (WGS) approach in 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) participants deeply phenotyped for platelet aggregation. We identify the RGS18 locus, which encodes a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with expression quantitative trait loci (eQTL) signatures for RGS18 expression in platelets. Gene-based approaches implicate the SVEP1 gene, a known contributor of coronary artery disease risk. Sentinel variants at RGS18 and PEAR1 are associated with thrombosis risk and increased gastrointestinal bleeding risk, respectively. Our WGS findings add to previously identified GWAS loci, provide insights regarding the mechanism(s) by which genetics may influence cardiovascular disease risk, and underscore the importance of rare variant and regulatory approaches to identifying loci contributing to complex phenotypes

Whole genome sequence analysis of blood lipid levels in \u3e66,000 individuals

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids

A linkage study of candidate loci in familial Parkinson's Disease

BACKGROUND: Parkinson's disease is the second most common neurodegenerative disorder after Alzheimer's disease. Most cases are sporadic, however familial cases do exist. We examined 12 families with familial Parkinson's disease ascertained at the Movement Disorder clinic at the Oregon Health Sciences University for genetic linkage to a number of candidate loci. These loci have been implicated in familial Parkinson's disease or in syndromes with a clinical presentation that overlaps with parkinsonism, as well as potentially in the pathogenesis of the disease. METHODS: The examined loci were PARK3, Parkin, DRD (dopa-responsive dystonia), FET1 (familial essential tremor), BDNF (brain-derived neurotrophic factor), GDNF (glial cell line-derived neurotrophic factor), Ret, DAT1 (the dopamine transporter), Nurr1 and Synphilin-1. Linkage to the α-synuclein gene and the Frontotemporal dementia with parkinsonism locus on chromosome 17 had previously been excluded in the families included in this study. Using Fastlink, Genehunter and Simwalk both parametric and model-free non-parametric linkage analyses were performed. RESULTS: In the multipoint parametric linkage analysis lod scores were below -2 for all loci except FET1 and Synphilin-1 under an autosomal dominant model with incomplete penetrance. Using non-parametric linkage analysis there was no evidence for linkage, although linkage could not be excluded. A few families showed positive parametric and non-parametric lod scores indicating possible genetic heterogeneity between families, although these scores did not reach any degree of statistical significance. CONCLUSIONS: We conclude that in these families there was no evidence for linkage to any of the loci tested, although we were unable to exclude linkage with both parametric and non-parametric methods

Neurocognitive impairment in type 2 diabetes: evidence for shared genetic aetiology

Aims/hypothesis: Type 2 diabetes is associated with cognitive impairments, but it is unclear whether common genetic factors influence both type 2 diabetes risk and cognition. Methods: Using data from 1892 Mexican-American individuals from extended pedigrees, including 402 with type 2 diabetes, we examined possible pleiotropy between type 2 diabetes and cognitive functioning, as measured by a comprehensive neuropsychological test battery. Results: Negative phenotypic correlations (ρp) were observed between type 2 diabetes and measures of attention (Continuous Performance Test [CPT d\u27]: ρp = -0.143, p = 0.001), verbal memory (California Verbal Learning Test [CVLT] recall: ρp = -0.111, p = 0.004) and face memory (Penn Face Memory Test [PFMT]: ρp = -0.127, p = 0.002; PFMT Delayed: ρp = -0.148, p = 2 × 10-4), replicating findings of cognitive impairment in type 2 diabetes. Negative genetic correlations (ρg) were also observed between type 2 diabetes and measures of attention (CPT d\u27: ρg = -0.401, p = 0.001), working memory (digit span backward test: ρg = -0.380, p = 0.005), and face memory (PFMT: ρg = -0.476, p = 2 × 10-4; PFMT Delayed: ρg = -0.376, p = 0.005), suggesting that the same genetic factors underlying risk for type 2 diabetes also influence poor cognitive performance in these domains. Performance in these domains was also associated with type 2 diabetes risk using an endophenotype ranking value approach. Specifically, on measures of attention (CPT d\u27: β = -0.219, p = 0.005), working memory (digit span backward: β = -0.326, p = 0.035), and face memory (PFMT: β = -0.171, p = 0.023; PFMT Delayed: β = -0.215, p = 0.005), individuals with type 2 diabetes showed the lowest performance, while unaffected/unrelated individuals showed the highest performance, and those related to an individual with type 2 diabetes performed at an intermediate level. Conclusions/interpretation: These findings suggest that cognitive impairment may be a useful endophenotype of type 2 diabetes and, therefore, help to elucidate the pathophysiological underpinnings of this chronic disease. Data availability: The data analysed in this study is available in dbGaP: www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001215.v2.p2

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown. Genetical genomics is an approach linking natural genetic variation to gene expression variation, thereby allowing the identification of genomic loci containing gene expression modulators (eQTLs). In this paper, we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains. We found that, while a significant number of eQTLs (365) had a consistent “static” regulatory effect on gene expression, an even larger number were found to be very sensitive to cell stage. As many as 1,283 eQTLs exhibited a “dynamic” behavior across cell types. By looking more closely at these dynamic eQTLs, we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes. These results stress the importance of studying gene expression variation in well-defined cell populations. Only such studies will be able to reveal the important differences in gene regulation between different ce

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.

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Extreme Clonality in Lymphoblastoid Cell Lines with Implications for Allele Specific Expression Analyses

Lymphoblastoid cell lines (LCL) are being actively and extensively used to examine the expression of specific genes and genome-wide expression profiles, including allele specific expression assays. However, it has recently been shown that approximately 10% of human genes exhibit random patterns of monoallelic expression within single clones of LCLs. Consequently allelic imbalance studies could be significantly compromised if bulk populations of donor cells are clonal, or near clonal. Here, using X chromosome inactivation as a readout, we confirm and quantify widespread near monoclonality in two independent sets of cell lines. Consequently, we recommend where possible the use of bulk, non cell line, ex vivo cells for allele specific expression assays