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1.  Targeted genetic testing for familial hypercholesterolaemia using next generation sequencing: a population-based study 
BMC Medical Genetics  2014;15:70.
Background
Familial hypercholesterolaemia (FH) is a common Mendelian condition which, untreated, results in premature coronary heart disease. An estimated 88% of FH cases are undiagnosed in the UK. We previously validated a method for FH mutation detection in a lipid clinic population using next generation sequencing (NGS), but this did not address the challenge of identifying index cases in primary care where most undiagnosed patients receive healthcare. Here, we evaluate the targeted use of NGS as a potential route to diagnosis of FH in a primary care population subset selected for hypercholesterolaemia.
Methods
We used microfluidics-based PCR amplification coupled with NGS and multiplex ligation-dependent probe amplification (MLPA) to detect mutations in LDLR, APOB and PCSK9 in three phenotypic groups within the Generation Scotland: Scottish Family Health Study including 193 individuals with high total cholesterol, 232 with moderately high total cholesterol despite cholesterol-lowering therapy, and 192 normocholesterolaemic controls.
Results
Pathogenic mutations were found in 2.1% of hypercholesterolaemic individuals, in 2.2% of subjects on cholesterol-lowering therapy and in 42% of their available first-degree relatives. In addition, variants of uncertain clinical significance (VUCS) were detected in 1.4% of the hypercholesterolaemic and cholesterol-lowering therapy groups. No pathogenic variants or VUCS were detected in controls.
Conclusions
We demonstrated that population-based genetic testing using these protocols is able to deliver definitive molecular diagnoses of FH in individuals with high cholesterol or on cholesterol-lowering therapy. The lower cost and labour associated with NGS-based testing may increase the attractiveness of a population-based approach to FH detection compared to genetic testing with conventional sequencing. This could provide one route to increasing the present low percentage of FH cases with a genetic diagnosis.
doi:10.1186/1471-2350-15-70
PMCID: PMC4083361  PMID: 24956927
Familial hypercholesterolaemia; Total cholesterol; LDLR; Molecular diagnostic testing; Next-generation sequencing; Primary care; Generation Scotland
2.  Molecular genetic contributions to socioeconomic status and intelligence 
Intelligence  2014;44(100):26-32.
Education, socioeconomic status, and intelligence are commonly used as predictors of health outcomes, social environment, and mortality. Education and socioeconomic status are typically viewed as environmental variables although both correlate with intelligence, which has a substantial genetic basis. Using data from 6815 unrelated subjects from the Generation Scotland study, we examined the genetic contributions to these variables and their genetic correlations. Subjects underwent genome-wide testing for common single nucleotide polymorphisms (SNPs). DNA-derived heritability estimates and genetic correlations were calculated using the ‘Genome-wide Complex Trait Analyses’ (GCTA) procedures. 21% of the variation in education, 18% of the variation in socioeconomic status, and 29% of the variation in general cognitive ability was explained by variation in common SNPs (SEs ~ 5%). The SNP-based genetic correlations of education and socioeconomic status with general intelligence were 0.95 (SE 0.13) and 0.26 (0.16), respectively. There are genetic contributions to intelligence and education with near-complete overlap between common additive SNP effects on these traits (genetic correlation ~ 1). Genetic influences on socioeconomic status are also associated with the genetic foundations of intelligence. The results are also compatible with substantial environmental contributions to socioeconomic status.
Highlights
•Generation Scotland is a large family-based cohort of ~ 24,000 people.•We investigate the genetic influences on education, SES, and intelligence.•Both DNA-based (subset of ~ 6500) and pedigree-based analyses are used.•Genetic effects on SES and education are linked to the genetic basis of intelligence.•There are also substantial environmental effects on all three traits.
doi:10.1016/j.intell.2014.02.006
PMCID: PMC4051988  PMID: 24944428
Generation Scotland; Intelligence; Education; Socioeconomic status; Genetics
3.  Common Genetic Variants Explain the Majority of the Correlation Between Height and Intelligence: The Generation Scotland Study 
Behavior Genetics  2014;44:91-96.
Greater height and higher intelligence test scores are predictors of better health outcomes. Here, we used molecular (single-nucleotide polymorphism) data to estimate the genetic correlation between height and general intelligence (g) in 6,815 unrelated subjects (median age 57, IQR 49–63) from the Generation Scotland: Scottish Family Health Study cohort. The phenotypic correlation between height and g was 0.16 (SE 0.01). The genetic correlation between height and g was 0.28 (SE 0.09) with a bivariate heritability estimate of 0.71. Understanding the molecular basis of the correlation between height and intelligence may help explain any shared role in determining health outcomes. This study identified a modest genetic correlation between height and intelligence with the majority of the phenotypic correlation being explained by shared genetic influences.
Electronic supplementary material
The online version of this article (doi:10.1007/s10519-014-9644-z) contains supplementary material, which is available to authorized users.
doi:10.1007/s10519-014-9644-z
PMCID: PMC3938855  PMID: 24554214
Height; Intelligence; Molecular genetics; Genetic correlation; Generation Scotland
4.  Pedigree and genotyping quality analyses of over 10,000 DNA samples from the Generation Scotland: Scottish Family Health Study 
BMC Medical Genetics  2013;14:38.
Background
Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based biobank of 24,000 participants with rich phenotype and DNA available for genetic research. This paper describes the laboratory results from genotyping 32 single nucleotide polymorphisms (SNPs) on DNA from over 10,000 participants who attended GS:SFHS research clinics. The analysis described here was undertaken to test the quality of genetic information available to researchers. The success rate of each marker genotyped (call rate), minor allele frequency and adherence to Mendelian inheritance are presented. The few deviations in marker transmission in the 925 parent-child trios analysed were assessed as to whether they were likely to be miscalled genotypes, data or sample handling errors, or pedigree inaccuracies including non-paternity.
Methods
The first 10,450 GS:SFHS clinic participants who had spirometry and smoking data available and DNA extracted were selected. 32 SNPs were assayed, chosen as part of a replication experiment from a Genome-Wide Association Study meta-analysis of lung function.
Results
In total 325,336 genotypes were returned. The overall project pass rate (32 SNPs on 10,450 samples) was 97.29%. A total of 925 parent-child trios were assessed for transmission of the SNP markers, with 16 trios indicating evidence of inconsistency in the recorded pedigrees.
Conclusions
The Generation Scotland: Scottish Family Health Study used well-validated study methods and can produce good quality genetic data, with a low error rate. The GS:SFHS DNA samples are of high quality and the family groups were recorded and processed with accuracy during collection of the cohort.
doi:10.1186/1471-2350-14-38
PMCID: PMC3614907  PMID: 23521772
Genetics; SNP Genotyping; Parent-child trios; Error rate; Non paternity; Generation Scotland; Biobank
5.  Generation Scotland: Donor DNA Databank; A control DNA resource 
BMC Medical Genetics  2010;11:166.
Background
Many medical disorders of public health importance are complex diseases caused by multiple genetic, environmental and lifestyle factors. Recent technological advances have made it possible to analyse the genetic variants that predispose to complex diseases. Reliable detection of these variants requires genome-wide association studies in sufficiently large numbers of cases and controls. This approach is often hampered by difficulties in collecting appropriate control samples. The Generation Scotland: Donor DNA Databank (GS:3D) aims to help solve this problem by providing a resource of control DNA and plasma samples accessible for research.
Methods
GS:3D participants were recruited from volunteer blood donors attending Scottish National Blood Transfusion Service (SNBTS) clinics across Scotland. All participants gave full written consent for GS:3D to take spare blood from their normal donation. Participants also supplied demographic data by completing a short questionnaire.
Results
Over five thousand complete sets of samples, data and consent forms were collected. DNA and plasma were extracted and stored. The data and samples were unlinked from their original SNBTS identifier number. The plasma, DNA and demographic data are available for research. New data obtained from analysis of the resource will be fed back to GS:3D and will be made available to other researchers as appropriate.
Conclusions
Recruitment of blood donors is an efficient and cost-effective way of collecting thousands of control samples. Because the collection is large, subsets of controls can be selected, based on age range, gender, and ethnic or geographic origin. The GS:3D resource should reduce time and expense for investigators who would otherwise have had to recruit their own controls.
doi:10.1186/1471-2350-11-166
PMCID: PMC3002899  PMID: 21092308
6.  Effect of Five Genetic Variants Associated with Lung Function on the Risk of Chronic Obstructive Lung Disease, and Their Joint Effects on Lung Function 
Rationale: Genomic loci are associated with FEV1 or the ratio of FEV1 to FVC in population samples, but their association with chronic obstructive pulmonary disease (COPD) has not yet been proven, nor have their combined effects on lung function and COPD been studied.
Objectives: To test association with COPD of variants at five loci (TNS1, GSTCD, HTR4, AGER, and THSD4) and to evaluate joint effects on lung function and COPD of these single-nucleotide polymorphisms (SNPs), and variants at the previously reported locus near HHIP.
Methods: By sampling from 12 population-based studies (n = 31,422), we obtained genotype data on 3,284 COPD case subjects and 17,538 control subjects for sentinel SNPs in TNS1, GSTCD, HTR4, AGER, and THSD4. In 24,648 individuals (including 2,890 COPD case subjects and 13,862 control subjects), we additionally obtained genotypes for rs12504628 near HHIP. Each allele associated with lung function decline at these six SNPs contributed to a risk score. We studied the association of the risk score to lung function and COPD.
Measurements and Main Results: Association with COPD was significant for three loci (TNS1, GSTCD, and HTR4) and the previously reported HHIP locus, and suggestive and directionally consistent for AGER and TSHD4. Compared with the baseline group (7 risk alleles), carrying 10–12 risk alleles was associated with a reduction in FEV1 (β = –72.21 ml, P = 3.90 × 10−4) and FEV1/FVC (β = –1.53%, P = 6.35 × 10−6), and with COPD (odds ratio = 1.63, P = 1.46 × 10−5).
Conclusions: Variants in TNS1, GSTCD, and HTR4 are associated with COPD. Our highest risk score category was associated with a 1.6-fold higher COPD risk than the population average score.
doi:10.1164/rccm.201102-0192OC
PMCID: PMC3398416  PMID: 21965014
FEV1; FVC; genome-wide association study; modeling risk

Résultats 1-6 (6)