Creating the 2011 area classification for output areas (2011 OAC)

This paper presents the methodology that has been used to create the 2011 Area Classification for Output Areas (2011 OAC). This extends a lineage of widely used public domain census only geodemographic classifications in the UK. It provides an update to the successful 2001 OAC methodology, and summarizes the social and physical structure of neighbourhoods using data from the 2011 UK Census. We also present the results of a user engagement exercise that underpinned the creation of an updated methodology for the 2011 OAC. The 2011 OAC comprises 8 Supergroups, 26 Groups and 76 Subgroups. Finally, we present an example of the results of the classification in Southampton

Variant biomarker discovery using mass spectrometry-based proteogenomics

Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants—including single nucleotide variants, frameshift variants, and mis-splicing isoforms—are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect

Genetic risk factors in Finnish patients with Parkinson's disease

Introduction Variation contributing to the risk of Parkinson's disease (PD) has been identified in several genes and at several loci including GBA, SMPD1, LRRK2, POLG1, CHCHD10 and MAPT, but the frequencies of risk variants seem to vary according to ethnic background. Our aim was to analyze how variation in these genes contributes to PD in the Finnish population. Methods The subjects consisted of 527 Finnish patients with early-onset PD, 325 patients with late-onset PD and 403 population controls. We screened for known genetic risk variants in GBA, SMPD1, LRRK2, POLG1, CHCHD10 and MAPT. In addition, DNA from 225 patients with early-onset Parkinson's disease was subjected to whole exome sequencing (WES). Results We detected a significant difference in the length variation of the CAG repeat in POLG1 between patients with early-onset PD compared to controls. The p.N370S and p.L444P variants in GBA contributed to a relative risk of 3.8 in early-onset PD and 2.5 in late-onset PD. WES revealed five variants in LRRK2 and SMPD1 that were found in the patients but not in the Finnish ExAC sequences. These are possible risk variants that require further confirmation. The p.G2019S variant in LRRK2, common in North African Arabs and Ashkenazi Jews, was not detected in any of the 849 PD patients. Conclusions The POLG1 CAG repeat length variation and the GBA p.L444P variant are associated with PD in the Finnish population.Peer reviewe

Estimating the causal influence of body mass index on risk of Parkinson disease: A Mendelian randomisation study.

BACKGROUND: Both positive and negative associations between higher body mass index (BMI) and Parkinson disease (PD) have been reported in observational studies, but it has been difficult to establish causality because of the possibility of residual confounding or reverse causation. To our knowledge, Mendelian randomisation (MR)-the use of genetic instrumental variables (IVs) to explore causal effects-has not previously been used to test the effect of BMI on PD. METHODS AND FINDINGS: Two-sample MR was undertaken using genome-wide association (GWA) study data. The associations between the genetic instruments and BMI were obtained from the GIANT consortium and consisted of the per-allele difference in mean BMI for 77 independent variants that reached genome-wide significance. The per-allele difference in log-odds of PD for each of these variants was estimated from a recent meta-analysis, which included 13,708 cases of PD and 95,282 controls. The inverse-variance weighted method was used to estimate a pooled odds ratio (OR) for the effect of a 5-kg/m2 higher BMI on PD. Evidence of directional pleiotropy averaged across all variants was sought using MR-Egger regression. Frailty simulations were used to assess whether causal associations were affected by mortality selection. A combined genetic IV expected to confer a lifetime exposure of 5-kg/m2 higher BMI was associated with a lower risk of PD (OR 0.82, 95% CI 0.69-0.98). MR-Egger regression gave similar results, suggesting that directional pleiotropy was unlikely to be biasing the result (intercept 0.002; p = 0.654). However, the apparent protective influence of higher BMI could be at least partially induced by survival bias in the PD GWA study, as demonstrated by frailty simulations. Other important limitations of this application of MR include the inability to analyse non-linear associations, to undertake subgroup analyses, and to gain mechanistic insights. CONCLUSIONS: In this large study using two-sample MR, we found that variants known to influence BMI had effects on PD in a manner consistent with higher BMI leading to lower risk of PD. The mechanism underlying this apparent protective effect warrants further study

A common genetic factor for Parkinson disease in ethnic Chinese population in Taiwan

BACKGROUND: Parkinson's disease (PD) is the most common neurodegenerative movement disorder, characterized clinically by resting tremor, bradykinesia, postural instability and rigidity. The prevalence of PD is approximately 2% of the population over 65 years of age and 1.7 million PD patients (age ≥ 55 years) live in China. Recently, a common LRRK2 variant Gly2385Arg was reported in ethnic Chinese PD population in Taiwan. We analyzed the frequency of this variant in our independent PD case-control population of Han Chinese from Taiwan. METHODS: 305 patients and 176 genetically unrelated healthy controls were examined by neurologists and the diagnosis of PD was based on the published criteria. The region of interest was amplified with standard polymerase chain reaction (PCR). PCR fragments then were directly sequenced in both forward and reverse directions. Differences in genotype frequencies between groups were assessed by the X(2 )test, while X(2 )analysis was used to test for the Hardy-Weinberg equilibrium. RESULTS: Of the 305 patients screened we identified 27 (9%) with heterozygous G2385R variant. This mutation was only found in 1 (0.5%) in our healthy control samples (odds ratio = 16.99, 95% CI: 2.29 to 126.21, p = 0.0002). Sequencing of the entire open reading frame of LRRK2 in G2385R carriers revealed no other variants. CONCLUSION: These data suggest that the G2385R variant contributes significantly to the etiology of PD in ethnic Han Chinese individuals. With consideration of the enormous and expanding aging Chinese population in mainland China and in Taiwan, this variant is probably the most common known genetic factor for PD worldwide

Using Simulation Models to Evaluate Ape Nest Survey Techniques

BACKGROUND: Conservationists frequently use nest count surveys to estimate great ape population densities, yet the accuracy and precision of the resulting estimates are difficult to assess. METHODOLOGY/PRINCIPAL FINDINGS: We used mathematical simulations to model nest building behavior in an orangutan population to compare the quality of the population size estimates produced by two of the commonly used nest count methods, the 'marked recount method' and the 'matrix method.' We found that when observers missed even small proportions of nests in the first survey, the marked recount method produced large overestimates of the population size. Regardless of observer reliability, the matrix method produced substantial overestimates of the population size when surveying effort was low. With high observer reliability, both methods required surveying approximately 0.26% of the study area (0.26 km(2) out of 100 km(2) in this simulation) to achieve an accurate estimate of population size; at or above this sampling effort both methods produced estimates within 33% of the true population size 50% of the time. Both methods showed diminishing returns at survey efforts above 0.26% of the study area. The use of published nest decay estimates derived from other sites resulted in widely varying population size estimates that spanned nearly an entire order of magnitude. The marked recount method proved much better at detecting population declines, detecting 5% declines nearly 80% of the time even in the first year of decline. CONCLUSIONS/SIGNIFICANCE: These results highlight the fact that neither nest surveying method produces highly reliable population size estimates with any reasonable surveying effort, though either method could be used to obtain a gross population size estimate in an area. Conservation managers should determine if the quality of these estimates are worth the money and effort required to produce them, and should generally limit surveying effort to 0.26% of the study area, unless specific management goals require more intensive sampling. Using site- and time- specific nest decay rates (or the marked recount method) are essential for accurate population size estimation. Marked recount survey methods with sufficient sampling effort hold promise for detecting population declines

Genetic Variability in CLU and Its Association with Alzheimer's Disease

Background: Recently, two large genome wide association studies in Alzheimer disease (AD) have identified variants in three different genes (CLU, PICALM and CR1) as being associated with the risk of developing AD. The strongest association was reported for an intronic single nucleotide polymorphism (SNP) in CLU.Methodology/Principal Findings: To further characterize this association we have sequenced the coding region of this gene in a total of 495 AD cases and 330 healthy controls. A total of twenty-four variants were found in both cases and controls. For the changes found in more than one individual, the genotypic frequencies were compared between cases and controls. Coding variants were found in both groups (including a nonsense mutation in a healthy subject), indicating that the pathogenicity of variants found in this gene must be carefully evaluated. We found no common coding variant associated with disease. In order to determine if common variants at the CLU locus effect expression of nearby (cis) mRNA transcripts, an expression quantitative loci (eQTL) analysis was performed. No significant eQTL associations were observed for the SNPs previously associated with AD.Conclusions/Significance: We conclude that common coding variability at this locus does not explain the association, and that there is no large effect of common genetic variability on expression in brain tissue. We surmise that the most likely mechanism underpinning the association is either small effects of genetic variability on resting gene expression, or effects on damage induced expression of the protein