Robustness of genome-wide scanning using archived dried blood spot samples as a DNA source

<p>Abstract</p> <p>Background</p> <p>The search to identify disease-susceptible genes requires access to biological material from numerous well-characterized subjects. Archived residual dried blood spot (DBS) samples, also known as Guthrie cards, from national newborn screening programs may provide a DNA source for entire populations. Combined with clinical information from medical registries, DBS samples could provide a rich source for productive research. However, the amounts of DNA which can be extracted from these precious samples are minute and may be prohibitive for numerous genotypings. Previously, we demonstrated that DBS DNA can be whole-genome amplified and used for reliable genetic analysis on different platforms, including genome-wide scanning arrays. However, it remains unclear whether this approach is workable on a large sample scale. We examined the robustness of using DBS samples for whole-genome amplification following genome-wide scanning, using arrays from Illumina and Affymetrix.</p> <p>Results</p> <p>This study is based on 4,641 DBS samples from the Danish Newborn Screening Biobank, extracted for three separate genome-wide association studies. The amount of amplified DNA was significantly (P < 0.05) affected by the year of storage and storage conditions. Nine (0.2%) DBS samples failed whole-genome amplification. A total of 4,586 (98.8%) samples met our criterion of success of a genetic call-rate above 97%. The three studies used different arrays, with mean genotyping call-rates of 99.385% (Illumina Infinium Human610-Quad), 99.722% (Illumina Infinium HD HumanOmni1-Quad), and 99.206% (Affymetrix Axiom Genome-Wide CEU). We observed a concordance rate of 99.997% in the 38 methodological replications, and 99.999% in the 27 technical replications. Handling variables such as time of storage, storage conditions and type of filter paper were shown too significantly (P < 0.05) affect the genotype call-rates in some of the arrays, although the effect was minimal.</p> <p>Conclusion</p> <p>Our study indicates that archived DBS samples from the Danish Newborn Screening Biobank represent a reliable resource of DNA for whole-genome amplification and subsequent genome-wide association studies. With call-rates equivalent to high quality DNA samples, our results point to new opportunities for using the neonatal biobanks available worldwide in the hunt for genetic components of disease.</p

Improved imputation quality of low-frequency and rare variants in European samples using the 'Genome of the Netherlands'

Although genome-wide association studies (GWAS) have identified many common variants associated with complex traits, low-frequency and rare variants have not been interrogated in a comprehensive manner. Imputation from dense reference panels, such as the 1000 Genomes Project (1000G), enables testing of ungenotyped variants for association. Here we present the results of imputation using a large, new population-specific panel: the Genome of The Netherlands (GoNL). We benchmarked the performance of the 1000G and GoNL reference sets by comparing imputation genotypes with 'true' genotypes typed on ImmunoChip in three European populations (Dutch, British, and Italian). GoNL showed significant improvement in the imputation quality for rare variants (MAF 0.05-0.5%) compared with 1000G. In Dutch samples, the mean observed Pearson correlation, r 2, increased from 0.61 to 0.71. W

Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus

Bone mineral density is known to be a heritable, polygenic trait whereas genetic variants contributing to lean mass variation remain largely unknown. We estimated the shared SNP heritability and performed a bivariate GWAS meta-analysis of total-body lean mass (TB-LM) and total-body less head bone mineral density (TBLH-BMD) regions in 10,414 children. The estimated SNP heritability is 43% for TBLH-BMD, and 39% for TB-LM, with a shared genetic component of 43%. We identify variants with pleiotropic effects in eight loci, including seven established bone mineral density loci: _WNT4, GALNT3, MEPE, CPED1/WNT16, TNFSF11, RIN3, and PPP6R3/LRP5_. Variants in the _TOM1L2/SREBF1_ locus exert opposing effects TB-LM and TBLH-BMD, and have a stronger association with the former trait. We show that _SREBF1_ is expressed in murine and human osteoblasts, as well as in human muscle tissue. This is the first bivariate GWAS meta-analysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass

Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus

Bone mineral density is known to be a heritable, polygenic trait whereas genetic variants contributing to lean mass variation remain largely unknown. We estimated the shared SNP heritability and performed a bivariate GWAS meta-analysis of total-body lean mass (TB-LM) and total-body less head bone mineral density (TBLH-BMD) regions in 10,414 children. The estimated SNP heritability is 43% for TBLH-BMD, and 39% for TB-LM, with a shared genetic component of 43%. We identify variants with pleiotropic effects in eight loci, including seven established bone mineral density loci: _WNT4, GALNT3, MEPE, CPED1/WNT16, TNFSF11, RIN3, and PPP6R3/LRP5_. Variants in the _TOM1L2/SREBF1_ locus exert opposing effects TB-LM and TBLH-BMD, and have a stronger association with the former trait. We show that _SREBF1_ is expressed in murine and human osteoblasts, as well as in human muscle tissue. This is the first bivariate GWAS meta-analysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass

Shared genetic variants suggest common pathways in allergy and autoimmune diseases.

BACKGROUND: The relationship between allergy and autoimmune disorders is complex and poorly understood. OBJECTIVE: To investigate commonalities in genetic loci and pathways between allergy and autoimmune diseases to elucidate shared disease mechanisms. METHODS: We meta-analyzed two GWAS on self-reported allergy and sensitization comprising a total of 62,330 individuals. These results were used to calculate enrichment for SNPs previously associated with autoimmune diseases. Furthermore, we probed for enrichment within genetic pathways and of transcription factor binding sites, and characterized commonalities in the variant burden on tissue-specific regulatory sites by calculating the enrichment of allergy SNPs falling in gene regulatory regions in various cells using Encode Roadmap DHS data, and compared the allergy data with all known diseases. RESULTS: Among 290 loci previously associated with 16 autoimmune diseases, we found a significant enrichment of loci also associated with allergy (p=1.4e-17) encompassing 29 loci at a false discovery rate<0.05. Such enrichment seemed to be a general characteristic for all autoimmune diseases. Among the common loci, 48% had the same direction of effect for allergy and autoimmune diseases. Additionally, we observed an enrichment of allergy SNPs falling within immune pathways and regions of chromatin accessible in immune cells that was also represented in autoimmune diseases, but not in other diseases. CONCLUSION: We identified shared susceptibility loci and commonalities in pathways between allergy and autoimmune diseases, suggesting shared diseases mechanisms. Further studies of these shared genetic mechanisms might help understanding the complex relationship between these diseases, including the parallel increase in disease prevalence

Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index

A large number of genetic loci are associated with adult body mass index. However, the genetics of childhood body mass index are largely unknown. We performed a meta-analysis of genome-wide association studies of childhood body mass index, using sex-and age-adjusted standard deviation scores. We included 35 668 children from 20 studies in the discovery phase and 11 873 children from 13 studies in the replication phase. In total, 15 loci reached genome-wide significance (P-value &lt;5 x 10(-8)) in the joint discovery and replication analysis, of which 12 are previously identified loci in or close to ADCY3, GNPDA2, TMEM18, SEC16B, FAIM2, FTO, TFAP2B, TNNI3K, MC4R, GPR61, LMX1B and OLFM4 associated with adult body mass index or childhood obesity. We identified three novel loci: rs13253111 near ELP3, rs8092503 near RAB27B and rs13387838 near ADAM23. Per additional risk allele, body mass index increased 0.04 Standard Deviation Score (SDS) [Standard Error (SE) 0.007], 0.05 SDS (SE 0.008) and 0.14 SDS (SE 0.025), for rs13253111, rs8092503 and rs13387838, respectively. A genetic risk score combining all 15 SNPs showed that each additional average risk allele was associated with a 0.073 SDS (SE 0.011, P-value = 3.12 x 10(-10)) increase in childhood body mass index in a population of 1955 children. This risk score explained 2% of the variance in childhood body mass index. This study highlights the shared genetic background between childhood and adult body mass index and adds three novel loci. These loci likely represent age-related differences in strength of the associations with body mass index.</p

Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics

Funding Information: Researchers were funded by investment from the European Regional Development Fund (ERDF) and the European Social Fund (ESF) Convergence Programme for Cornwall and the Isles of Scilly [J.T.]; European Research Council (ERC) [grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC to T.M.F., A.R.W.], [ERC Consolidator Grant, ERC-2014-CoG-648916 to V.W.V.J.], [P.R.N.]; University of Bergen, KG Jebsen and Helse Vest [P.R.N.]; Wellcome Trust Senior Investigator Awards [A.T.H. (WT098395), M.I.M. (WT098381)]; National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0611–10219); Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150) [R.M.F., R.N.B.]; 4-year studentship (Grant Code: WT083431MF) [R.C.R]; the European Research Council under the European Union’s Seventh Framework Programme (FP/2007– 2013)/ERC Grant Agreement (grant number 669545; Develop Obese) [D.A.L.]; US National Institute of Health (grant: R01 DK10324) [D.A.L, C.L.R]; Wellcome Trust GWAS grant (WT088806) [D.A.L] and NIHR Senior Investigator Award (NF-SI-0611–10196) [D.A.L]; Wellcome Trust Institutional Strategic Support Award (WT097835MF) [M.A.T.]; The Diabetes Research and Wellness Foundation Non-Clinical Fellowship [J.T.]; Australian National Health and Medical Research Council Early Career Fellowship (APP1104818) [N.M.W.]; Daniel B. Burke Endowed Chair for Diabetes Research [S.F.A.G.]; UK Medical Research Council Unit grants MC_UU_12013_5 [R.C.R, L.P, S.R, C.L.R, D.M.E., D.A.L.] and MC_UU_12013_4 [D.M.E.]; Medical Research Council (grant: MR/M005070/1) [M.N.W., S.E.J.]; Australian Research Council Future Fellowship (FT130101709) [D.M.E] and (FT110100548) [S.E.M.]; NIHR Oxford Biomedical Research Centre (BRC); Oak Foundation Fellowship and Novo Nordisk Foundation (12955) [B.F.]; FRQS research scholar and Clinical Scientist Award by the Canadian Diabetes Association and the Maud Menten Award from the Institute of Genetics– Canadian Institute of Health Research (CIHR) [MFH]; CIHR— Frederick Banting and Charles Best Canada Graduate Scholarships [C.A.]; FRQS [L.B.]; Netherlands Organization for Health Research and Development (ZonMw–VIDI 016.136.361) [V.W.V.J.]; National Institute on Aging (R01AG29451) [J.M.M.]; 2010–2011 PRIN funds of the University of Ferrara—Holder: Prof. Guido Barbujani, Supervisor: Prof. Chiara Scapoli—and in part sponsored by the European Foundation for the Study of Diabetes (EFSD) Albert Renold Travel Fellowships for Young Scientists, ‘5 per mille’ contribution assigned to the University of Ferrara, income tax return year 2009 and the ENGAGE Exchange and Mobility Program for ENGAGE training funds, ENGAGE project, grant agreement HEALTH-F4–2007-201413 [L.M.]; ESRC (RES-060–23-0011) [C.L.R.]; National Institute of Health Research ([S.D., M.I.M.], Senior Investigator Award (NF-SI-0611–10196) [D.A.L]); Australian NHMRC Fellowships Scheme (619667) [G.W.M]. For study-specific funding, please see Supplementary Material. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the Charity Open Access Fund (COAF). Funding Information: We are extremely grateful to the participants and families who contributed to all of the studies and the teams of investigators involved in each one. These include interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. This research has been conducted using the UK Biobank Resource (Application numbers 7036 and 12703). For additional study-specific acknowledgements, please see Supplementary Material. Conflict of Interest statement. D.A.L. has received support from Roche Diagnostics and Medtronic for biomarker research unrelated to the work presented here. Funding Researchers were funded by investment from the European Regional Development Fund (ERDF) and the European Social Fund (ESF) Convergence Programme for Cornwall and the Isles of Scilly [J.T.]; European Research Council (ERC) [grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC to T.M.F., A.R.W.], [ERC Consolidator Grant, ERC-2014-CoG-648916 to V.W.V.J.], [P.R.N.]; University of Bergen, KG Jebsen and Helse Vest [P.R.N.]; Wellcome Trust Senior Investigator Awards [A.T.H. (WT098395), M.I.M. (WT098381)]; National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0611-10219); Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150) [R.M.F., R.N.B.]; 4-year studentship (Grant Code: WT083431MF) [R.C.R]; the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement (grant number 669545; Develop Obese) [D.A.L.]; US National Institute of Health (grant: R01 DK10324) [D.A.L, C.L.R]; Wellcome Trust GWAS grant (WT088806) [D.A.L] and NIHR Senior Investigator Award (NF-SI-0611-10196) [D.A.L]; Wellcome Trust Institutional Strategic Support Award (WT097835MF) [M.A.T.]; The Diabetes Research and Wellness Foundation Non-Clinical Fellowship [J.T.]; Australian National Health and Medical Research Council Early Career Fellowship (APP1104818) [N.M.W.]; Daniel B. Burke Endowed Chair for Diabetes Research [S.F.A.G.]; UK Medical Research Council Unit grants MC_UU_12013_5 [R.C.R, L.P, S.R, C.L.R, D.M.E., D.A.L.] and MC_UU_12013_4 [D.M.E.]; Medical Research Council (grant: MR/M005070/1) [M.N.W., S.E.J.]; Australian Research Council Future Fellowship (FT130101709) [D.M.E] and (FT110100548) [S.E.M.]; NIHR Oxford Biomedical Research Centre (BRC); Oak Foundation Fellowship and Novo Nordisk Foundation (12955) [B.F.]; FRQS research scholar and Clinical Scientist Award by the Canadian Diabetes Association and the Maud Menten Award from the Institute of Genetics-Canadian Institute of Health Research (CIHR) [MFH]; CIHR-Frederick Banting and Charles Best Canada Graduate Scholarships [C.A.]; FRQS [L.B.]; Netherlands Organization for Health Research and Development (ZonMw-VIDI 016.136.361) [V.W.V.J.]; National Institute on Aging (R01AG29451) [J.M.M.]; 2010-2011 PRIN funds of the University of Ferrara-Holder: Prof. Guido Barbujani, Supervisor: Prof. Chiara Scapoli-and in part sponsored by the European Foundation for the Study of Diabetes (EFSD) Albert Renold Travel Fellowships for Young Scientists, '5 per mille' contribution assigned to the University of Ferrara, income tax return year 2009 and the ENGAGE Exchange and Mobility Program for ENGAGE training funds, ENGAGE project, grant agreement HEALTH-F4-2007-201413 [L.M.]; ESRC (RES-060-23-0011) [C.L.R.]; National Institute of Health Research ([S.D., M.I.M.], Senior Investigator Award (NFSI-0611-10196) [D.A.L]); Australian NHMRC Fellowships Scheme (619667) [G.W.M]. For study-specific funding, please see Supplementary Material. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the Charity Open Access Fund (COAF). Publisher Copyright: © The Author(s) 2018.Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P<5 x 10(-8). In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.Peer reviewe

Improved imputation quality of low-frequency and rare variants in European samples using the ‘Genome of The Netherlands'

Although genome-wide association studies (GWAS) have identified many common variants associated with complex traits, low-frequency and rare variants have not been interrogated in a comprehensive manner. Imputation from dense reference panels, such as the 1000 Genomes Project (1000G), enables testing of ungenotyped variants for association. Here we present the results of imputation using a large, new population-specific panel: the Genome of The Netherlands (GoNL). We benchmarked the performance of the 1000G and GoNL reference sets by comparing imputation genotypes with ‘true' genotypes typed on ImmunoChip in three European populations (Dutch, British, and Italian). GoNL showed significant improvement in the imputation quality for rare variants (MAF 0.05–0.5%) compared with 1000G. In Dutch samples, the mean observed Pearson correlation, r2, increased from 0.61 to 0.71. We also saw improved imputation accuracy for other European populations (in the British samples, r2 improved from 0.58 to 0.65, and in the Italians from 0.43 to 0.47). A combined reference set comprising 1000G and GoNL improved the imputation of rare variants even further. The Italian samples benefitted the most from this combined reference (the mean r2 increased from 0.47 to 0.50). We conclude that the creation of a large population-specific reference is advantageous for imputing rare variants and that a combined reference panel across multiple populations yields the best imputation results