Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits.

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.

Purifying selection on noncoding deletions of human regulatory loci detected using their cellular pleiotropy.

Genomic deletions provide a powerful loss-of-function model in noncoding regions to assess the role of purifying selection on genetic variation. Regulatory element function is characterized by nonuniform tissue and cell type activity, necessarily linking the study of fitness consequences from regulatory variants to their corresponding cellular activity. We generated a callset of deletions from genomes in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and used deletions from The 1000 Genomes Project Consortium (1000GP) in order to examine whether purifying selection preserves noncoding sites of chromatin accessibility marked by DNase I hypersensitivity (DHS), histone modification (enhancer, transcribed, Polycomb-repressed, heterochromatin), and chromatin loop anchors. To examine this in a cellular activity-aware manner, we developed a statistical method, pleiotropy ratio score (PlyRS), which calculates a correlation-adjusted count of "cellular pleiotropy" for each noncoding base pair by analyzing shared regulatory annotations across tissues and cell types. By comparing real deletion PlyRS values to simulations in a length-matched framework and by using genomic covariates in analyses, we found that purifying selection acts to preserve both DHS and enhancer noncoding sites. However, we did not find evidence of purifying selection for noncoding transcribed, Polycomb-repressed, or heterochromatin sites beyond that of the noncoding background. Additionally, we found evidence that purifying selection is acting on chromatin loop integrity by preserving colocalized CTCF binding sites. At regions of DHS, enhancer, and CTCF within chromatin loop anchors, we found evidence that both sites of activity specific to a particular tissue or cell type and sites of cellularly pleiotropic activity are preserved by selection.

Mitigating false-positive associations in rare disease gene discovery.

Clinical sequencing is expanding, but causal variants are still not identified in the majority of cases. These unsolved cases can aid in gene discovery when individuals with similar phenotypes are identified in systems such as the Matchmaker Exchange. We describe risks for gene discovery in this growing set of unsolved cases. In a set of rare disease cases with the same phenotype, it is not difficult to find two individuals with the same phenotype that carry variants in the same gene. We quantify the risk of false-positive association in a cohort of individuals with the same phenotype, using the prior probability of observing a variant in each gene from over 60,000 individuals (Exome Aggregation Consortium). Based on the number of individuals with a genic variant, cohort size, specific gene, and mode of inheritance, we calculate a P value that the match represents a true association. A match in two of 10 patients in MECP2 is statistically significant (P = 0.0014), whereas a match in TTN would not reach significance, as expected (P > 0.999). Finally, we analyze the probability of matching in clinical exome cases to estimate the number of cases needed to identify genes related to different disorders. We offer Rare Disease Match, an online tool to mitigate the uncertainty of false-positive associations.

TMEM106B, the risk gene for frontotemporal dementia, is regulated by the microRNA-132/212 cluster and affects progranulin pathways.

Frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP) is a fatal neurodegenerative disease with no available treatments. Mutations in the progranulin gene (GRN) causing impaired production or secretion of progranulin are a common Mendelian cause of FTLD-TDP; additionally, common variants at chromosome 7p21 in the uncharacterized gene TMEM106B were recently linked by genome-wide association to FTLD-TDP with and without GRN mutations. Here we show that TMEM106B is neuronally expressed in postmortem human brain tissue, and that expression levels are increased in FTLD-TDP brain. Furthermore, using an unbiased, microarray-based screen of >800 microRNAs (miRs), we identify microRNA-132 as the top microRNA differentiating FTLD-TDP and control brains, with <50% normal expression levels of three members of the microRNA-132 cluster (microRNA-132, microRNA-132*, and microRNA-212) in disease. Computational analyses, corroborated empirically, demonstrate that the top mRNA target of both microRNA-132 and microRNA-212 is TMEM106B; both microRNAs repress TMEM106B expression through shared microRNA-132/212 binding sites in the TMEM106B 3'UTR. Increasing TMEM106B expression to model disease results in enlargement and poor acidification of endo-lysosomes, as well as impairment of mannose-6-phosphate-receptor trafficking. Finally, endogenous neuronal TMEM106B colocalizes with progranulin in late endo-lysosomes, and TMEM106B overexpression increases intracellular levels of progranulin. Thus, TMEM106B is an FTLD-TDP risk gene, with microRNA-132/212 depression as an event which can lead to aberrant overexpression of TMEM106B, which in turn alters progranulin pathways. Evidence for this pathogenic cascade includes the striking convergence of two independent, genomic-scale screens on a microRNA:mRNA regulatory pair. Our findings open novel directions for elucidating miR-based therapies in FTLD-TDP.

Fetal alcohol spectrum disorder predisposes to metabolic abnormalities in adulthood.

Prenatal alcohol exposure (PAE) affects at least 10% of newborns globally and leads to the development of fetal alcohol spectrum disorders (FASDs). Despite its high incidence, there is no consensus on the implications of PAE on metabolic disease risk in adults. Here, we describe a cohort of adults with FASDs that had an increased incidence of metabolic abnormalities, including type 2 diabetes, low HDL, high triglycerides, and female-specific overweight and obesity. Using a zebrafish model for PAE, we performed population studies to elucidate the metabolic disease seen in the clinical cohort. Embryonic alcohol exposure (EAE) in male zebrafish increased the propensity for diet-induced obesity and fasting hyperglycemia in adulthood. We identified several consequences of EAE that may contribute to these phenotypes, including a reduction in adult locomotor activity, alterations in visceral adipose tissue and hepatic development, and persistent diet-responsive transcriptional changes. Taken together, our findings define metabolic vulnerabilities due to EAE and provide evidence that behavioral changes and primary organ dysfunction contribute to resultant metabolic abnormalities.

Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy.

The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet-Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes-a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders.

When "N of 2" is not enough: integrating statistical and functional data in gene discovery.

The expanding use of genomic sequencing promises to improve clinical diagnostics and to drive the discovery of new disease genes. Candidate genes are increasingly being identified through recurrent cases (e.g., two or more independent cases ["N of 2"] in which variants are present in the same gene). These second case hits provide statistical evidence of an association, which may then be combined with functional validation or familial segregation studies to bolster the evidence that a gene is truly causal. Here, we discuss how to integrate different forms of functional evidence with human genetics case and segregation data to improve the significance of new disease-gene associations.