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A fundamental challenge in the post-genome era is to understand and annotate the consequences of genetic variation, particularly within the context of human tissues. We present a set of integrated experiments that investigate the effects of common genetic variability on DNA methylation and mRNA expression in four human brain regions each from 150 individuals (600 samples total). We find an abundance of genetic cis regulation of mRNA expression and show for the first time abundant quantitative trait loci for DNA CpG methylation across the genome. We show peak enrichment for cis expression QTLs to be approximately 68,000 bp away from individual transcription start sites; however, the peak enrichment for cis CpG methylation QTLs is located much closer, only 45 bp from the CpG site in question. We observe that the largest magnitude quantitative trait loci occur across distinct brain tissues. Our analyses reveal that CpG methylation quantitative trait loci are more likely to occur for CpG sites outside of islands. Lastly, we show that while we can observe individual QTLs that appear to affect both the level of a transcript and a physically close CpG methylation site, these are quite rare. We believe these data, which we have made publicly available, will provide a critical step toward understanding the biological effects of genetic variation.

The genetic cause of amyotrophic lateral sclerosis (ALS) is not well understood. Finland is a well suited location for a genome-wide association study of ALS because the incidence of the disease is one of the highest in the world, and because the genetic homogeneity of the Finnish population enhances the ability to detect risk loci. We aimed to identify genetic risk factors for ALS in the Finnish population. We did a genome-wide association study of Finnish patients with ALS and control individuals by use of Illumina genome-wide genotyping arrays. DNA was collected from patients who attended an ALS specialty clinic that receives referrals from neurologists throughout Finland. Control samples were from a population-based study of elderly Finnish individuals. Patients known to carry D90A alleles of the SOD1 gene (n=40) were included in the final analysis as positive controls to assess whether our genome-wide association study was able to detect an association signal at this locus. We obtained samples from 442 patients with ALS and 521 control individuals. After quality control filters were applied, 318 167 single nucleotide polymorphisms (SNPs) from 405 people with ALS and 497 control individuals were available for analysis. We identified two association peaks that exceeded genome-wide significance. One was located on chromosome 21q22 (rs13048019, p=2·58×10 −8), which corresponds to the autosomal recessive D90A allele of the SOD1 gene. The other was detected in a 232 kb block of linkage disequilibrium (rs3849942, p=9·11×10 −11) in a region of chromosome 9p that was previously identified in linkage studies of families with ALS. Within this region, we defined a 42-SNP haplotype that was associated with significantly increased risk of ALS (p=7·47×10 −33 when people with familial ALS were compared with controls, odds ratio 21·0, 95% CI 11·2–39·1) and which overlapped with an association locus recently reported for frontotemporal dementia. For the 93 patients with familial ALS, the population attributable risk for the chromosome 9p21 locus was 37·9% (95% CI 27·7–48·1) and that for D90A homozygosity was 25·5% (16·9–34·1). The chromosome 9p21 locus is a major cause of familial ALS in the Finnish population. Our data suggest the presence of a founder mutation for chromosome 9p21-linked ALS. Furthermore, the overlap with the risk haplotype recently reported for frontotemporal dementia provides further evidence of a shared genetic cause for these two neurodegenerative diseases. National Institutes of Health and National Institute on Aging, Microsoft Research, ALS Association, Helsinki University Central Hospital, Finnish Academy, Finnish Medical Society Duodecim, and Kuopio University.

Genome-wide association (GWA) studies have been limited by the reliance on common variants present on microarrays or imputable from the HapMap Project data. More recently, the completion of the 1000 Genomes Project has provided variant and haplotype information for several million variants derived from sequencing over 1,000 individuals. To help understand the extent to which more variants (including low frequency (1% ≤ MAF <5%) and rare variants (<1%)) can enhance previously identified associations and identify novel loci, we selected 93 quantitative circulating factors where data was available from the InCHIANTI population study. These phenotypes included cytokines, binding proteins, hormones, vitamins and ions. We selected these phenotypes because many have known strong genetic associations and are potentially important to help understand disease processes. We performed a genome-wide scan for these 93 phenotypes in InCHIANTI. We identified 21 signals and 33 signals that reached P<5×10(-8) based on HapMap and 1000 Genomes imputation, respectively, and 9 and 11 that reached a stricter, likely conservative, threshold of P<5×10(-11) respectively. Imputation of 1000 Genomes genotype data modestly improved the strength of known associations. Of 20 associations detected at P<5×10(-8) in both analyses (17 of which represent well replicated signals in the NHGRI catalogue), six were captured by the same index SNP, five were nominally more strongly associated in 1000 Genomes imputed data and one was nominally more strongly associated in HapMap imputed data. We also detected an association between a low frequency variant and phenotype that was previously missed by HapMap based imputation approaches. An association between rs112635299 and alpha-1 globulin near the SERPINA gene represented the known association between rs28929474 (MAF = 0.007) and alpha1-antitrypsin that predisposes to emphysema (P = 2.5×10(-12)). Our data provide important proof of principle that 1000 Genomes imputation will detect novel, low frequency-large effect associations.

Background: Aging skeletal muscle is characterized not only by a reduction in size (sarcopenia) and strength but also by an increase in fatty infiltration (myosteatosis). An effective countermeasure to sarcopenia is resistance exercise; however, its effect on fatty infiltration is less clear. Objective: To examine in resistance-trained older persons whether muscle attenuation, a noninvasive measure of muscle density reflecting intramuscular lipid content, is altered with training status. Methods: Thirteen healthy community-dwelling men and women aged 65–83 years (body mass index 27.0 ± 1.2, mean ± SE) had computed-tomography scans of the mid-thigh performed following 24 weeks of training, 24 weeks of detraining, and 12 weeks of retraining. Training and retraining were undertaken twice weekly for several upper- and lower-body muscle groups. Skeletal muscle attenuation in Hounsfield units (HU) as well as mid-thigh muscle volume was obtained for the quadriceps and hamstrings. Muscle strength was assessed by 1-repetition maximum and physical function by a battery of tests. Results: The average change in muscle strength following training, detraining and retraining was 48.8 ± 2.9%, –17.6 ± 1.3%, and 19.8 ± 2.0%, respectively. Strength changes were accompanied by significant alterations in muscle density (p < 0.001), with the quadriceps HU decreasing by 7.7 ± 1.0% following detraining and increasing by 5.4 ± 0.5% with retraining. For the hamstrings HU measure, detraining and retraining resulted in an 11.9 ± 1.4% loss and a 5.5 ± 1.8% gain, respectively. There was no significant change in muscle volume. Conclusion: Cessation of resistance exercise in trained older persons increases the fatty infiltration of muscle, while resumption of exercise decreases it. Monitoring changes in both muscle size and fat infiltration may enable a more comprehensive assessment of exercise in combating age-related muscular changes.

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with cross-sectional blood pressure (BP) traits; however, GWAS based on longitudinal BP have been underexplored. We performed ethnic-specific and trans-ethnic GWAS meta-analysis using longitudinal and cross-sectional BP data of 33,720 individuals from five cohorts in the US and one in Brazil. In addition to identifying several known loci, we identified thirteen novel loci with nine based on longitudinal and four on cross-sectional BP traits. Most of the novel loci were ethnic- or study-specific, with the majority identified in African Americans (AA). Four of these discoveries showed additional evidence of association in independent datasets, including an intergenic variant (rs4060030, p  = 7.3 × 10 –9 ) with reported regulatory function. We observed a high correlation between the meta-analysis results for baseline and longitudinal average BP ( rho  = 0.48). BP trajectory results were more correlated with those of average BP ( rho  = 0.35) than baseline BP( rho  = 0.18). Heritability estimates trended higher for longitudinal traits than for cross-sectional traits, providing evidence for different genetic architectures. Furthermore, the longitudinal data identified up to 20% more BP known associations than did cross-sectional data. Our analyses of longitudinal BP data in diverse ethnic groups identified novel BP loci associated with BP trajectory, indicating a need for further longitudinal GWAS on BP and other age-related traits.

This research investigates the influence of demographic factors on human genetic sub-structure. In our discovery cohort, we show significant demographic trends for decreasing autozygosity associated with population variation in chronological age. Autozygosity, the genomic signature of consanguinity, is identifiable on a genome-wide level as extended tracts of homozygosity. We identified an average of 28.6 tracts of extended homozygosity greater than 1 Mb in length in a representative population of 809 unrelated North Americans of European descent ranging in chronological age from 19-99 years old. These homozygous tracts made up a population average of 42 Mb of the genome corresponding to 1.6% of the entire genome, with each homozygous tract an average of 1.5 Mb in length. Runs of homozygosity are steadily decreasing in size and frequency as time progresses (linear regression, p<0.05). We also calculated inbreeding coefficients and showed a significant trend for population-wide increasing heterozygosity outside of linkage disequilibrium. We successfully replicated these associations in a demographically similar cohort comprised of a subgroup of 477 Baltimore Longitudinal Study of Aging participants. We also constructed statistical models showing predicted declining rates of autozygosity spanning the 20th century. These predictive models suggest a 14.0% decrease in the frequency of these runs of homozygosity and a 24.3% decrease in the percent of the genome in runs of homozygosity, as well as a 30.5% decrease in excess homozygosity based on the linkage pruned inbreeding coefficients. The trend for decreasing autozygosity due to panmixia and larger effective population sizes will likely affect the frequency of rare recessive genetic diseases in the future. Autozygosity has declined, and it seems it will continue doing so.

Association studies have identified several signals at the LRRK2 locus for Parkinson's disease (PD), Crohn's disease (CD) and leprosy. However, little is known about the molecular mechanisms mediating these effects. To further characterize this locus, we fine-mapped the risk association in 5,802 PD and 5,556 controls using a dense genotyping array (ImmunoChip). Using samples from 134 post-mortem control adult human brains (UK Human Brain Expression Consortium), where up to ten brain regions were available per individual, we studied the regional variation, splicing and regulation of LRRK2. We found convincing evidence for a common variant PD association located outside of the LRRK2 protein coding region (rs117762348, A>G, P = 2.56×10(-8), case/control MAF 0.083/0.074, odds ratio 0.86 for the minor allele with 95% confidence interval [0.80-0.91]). We show that mRNA expression levels are highest in cortical regions and lowest in cerebellum. We find an exon quantitative trait locus (QTL) in brain samples that localizes to exons 32-33 and investigate the molecular basis of this eQTL using RNA-Seq data in n = 8 brain samples. The genotype underlying this eQTL is in strong linkage disequilibrium with the CD associated non-synonymous SNP rs3761863 (M2397T). We found two additional QTLs in liver and monocyte samples but none of these explained the common variant PD association at rs117762348. Our results characterize the LRRK2 locus, and highlight the importance and difficulties of fine-mapping and integration of multiple datasets to delineate pathogenic variants and thus develop an understanding of disease mechanisms.

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call 'trait prioritized sub-networks.' As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn's disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn's disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.

Mutations within the NOTCH3 gene cause cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). CADASIL mutations appear to be restricted to the first twenty-four exons, resulting in the gain or loss of a cysteine amino acid. The role of other exonic NOTCH3 variation not involving cysteine residues and mutations in exons 25-33 in ischemic stroke remains unresolved. All 33 exons of NOTCH3 were sequenced in 269 Caucasian probands from the Siblings With Ischemic Stroke Study (SWISS), a 70-center North American affected sibling pair study and 95 healthy Caucasian control subjects. Variants identified by sequencing in the SWISS probands were then tested for association with ischemic stroke using US Caucasian controls collected at the Mayo Clinic (n=654), and further assessed in a Caucasian (n=802) and African American (n=298) patient-control series collected through the Ischemic Stroke Genetics Study (ISGS). Sequencing of the 269 SWISS probands identified one (0.4%) with small vessel type stroke carrying a known CADASIL mutation (p.R558C; Exon 11). Of the 19 common NOTCH3 variants identified, the only variant significantly associated with ischemic stroke after multiple testing adjustment was p.R1560P (rs78501403; Exon 25) in the combined SWISS and ISGS Caucasian series (Odds Ratio [OR] 0.50, P=0.0022) where presence of the minor allele was protective against ischemic stroke. Although only significant prior to adjustment for multiple testing, p.T101T (rs3815188; Exon 3) was associated with an increased risk of small-vessel stroke (OR: 1.56, P=0.008) and p.P380P (rs61749020; Exon 7) was associated with decreased risk of large-vessel stroke (OR: 0.35, P=0.047) in Caucasians. No significant associations were observed in the small African American series. Cysteine-affecting NOTCH3 mutations are rare in patients with typical ischemic stroke, however our observation that common NOTCH3 variants may be associated with risk of ischemic stroke warrants further study.

A major goal of personalized medicine is to pre-symptomatically identify individuals at high risk for disease using knowledge of each individual's particular genetic profile and constellation of environmental risk factors. With the identification of several well-replicated risk factors for age-related macular degeneration (AMD), the leading cause of legal blindness in older adults, this previously unreachable goal is beginning to seem less elusive. However, recently developed algorithms have either been much less accurate than expected, given the strong effects of the identified risk factors, or have not been applied to independent datasets, leaving unknown how well they would perform in the population at large. We sought to increase accuracy by using novel modeling strategies, including multifactor dimensionality reduction (MDR) and grammatical evolution of neural networks (GENN), in addition to the traditional logistic regression approach. Furthermore, we rigorously designed and tested our models in three distinct datasets: a Vanderbilt-Miami (VM) clinic-based case-control dataset, a VM family dataset, and the population-based Age-related Maculopathy Ancillary (ARMA) Study cohort. Using a consensus approach to combine the results from logistic regression and GENN models, our algorithm was successful in differentiating between high- and low-risk groups (sensitivity 77.0%, specificity 74.1%). In the ARMA cohort, the positive and negative predictive values were 63.3% and 70.7%, respectively. We expect that future efforts to refine this algorithm by increasing the sample size available for model building, including novel susceptibility factors as they are discovered, and by calibrating the model for diverse populations will improve accuracy.