DINGO: increasing the power of locus discovery in maternal and fetal genome-wide association studies of perinatal traits.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
26 Oct 2024
26 Oct 2024
Historique:
received:
04
09
2023
accepted:
14
10
2024
medline:
27
10
2024
pubmed:
27
10
2024
entrez:
27
10
2024
Statut:
epublish
Résumé
Perinatal traits are influenced by fetal and maternal genomes. We investigate the performance of three strategies to detect loci in maternal and fetal genome-wide association studies (GWASs) of the same quantitative trait: (i) the traditional strategy of analysing maternal and fetal GWASs separately; (ii) a two-degree-of-freedom test which combines information from maternal and fetal GWASs; and (iii) a one-degree-of-freedom test where signals from maternal and fetal GWASs are meta-analysed together conditional on estimated sample overlap. We demonstrate that the optimal strategy depends on the extent of sample overlap, correlation between phenotypes, whether loci exhibit fetal and/or maternal effects, and whether these effects are directionally concordant. We apply our methods to summary statistics from a recent GWAS meta-analysis of birth weight. Both the two-degree-of-freedom and meta-analytic approaches increase the number of genetic loci for birth weight relative to separately analysing the scans. Our best strategy identifies an additional 62 loci compared to the most recently published meta-analysis of birth weight. We conclude that whilst the two-degree-of-freedom test may be useful for the analysis of certain perinatal phenotypes, for most phenotypes, a simple meta-analytic strategy is likely to perform best, particularly in situations where maternal and fetal GWASs only partially overlap.
Identifiants
pubmed: 39461952
doi: 10.1038/s41467-024-53495-9
pii: 10.1038/s41467-024-53495-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9255Subventions
Organisme : Department of Health | National Health and Medical Research Council (NHMRC)
ID : GNT1157714
Organisme : Department of Health | National Health and Medical Research Council (NHMRC)
ID : 2017942
Organisme : Department of Health | National Health and Medical Research Council (NHMRC)
ID : 2008723
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : DE240100014
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : FT220100069
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : DE220101226
Organisme : Wellcome Trust (Wellcome)
ID : WT220390
Informations de copyright
© 2024. The Author(s).
Références
Beaumont, R. N. et al. 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. Hum. Mol. Genet. 27, 742–756 (2018).
pubmed: 29309628
pmcid: 5886200
doi: 10.1093/hmg/ddx429
Freathy, R. M. et al. Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight. Nat. Genet. 42, 430–435 (2010).
pubmed: 20372150
pmcid: 2862164
doi: 10.1038/ng.567
Horikoshi, M. et al. Genome-wide associations for birth weight and correlations with adult disease. Nature 538, 248–252 (2016).
pubmed: 27680694
pmcid: 5164934
doi: 10.1038/nature19806
Horikoshi, M. et al. New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism. Nat. Genet. 45, 76–82 (2013).
pubmed: 23202124
doi: 10.1038/ng.2477
Juliusdottir, T. et al. Distinction between the effects of parental and fetal genomes on fetal growth. Nat. Genet. 53, 1135–1142 (2021).
pubmed: 34282336
doi: 10.1038/s41588-021-00896-x
Warrington, N. M. et al. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat. Genet. 51, 804–814 (2019).
pubmed: 31043758
pmcid: 6522365
doi: 10.1038/s41588-019-0403-1
Warrington, N. M., Freathy, R. M., Neale, M. C. & Evans, D. M. Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank. Int. J. Epidemiol. 47, 1229–1241 (2018).
pubmed: 29447406
pmcid: 6124616
doi: 10.1093/ije/dyy015
Liu, X. et al. Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 associate with gestational duration. Nat. Commun. 10, 3927 (2019).
pubmed: 31477735
pmcid: 6718389
doi: 10.1038/s41467-019-11881-8
Zhang, G. et al. Genetic associations with gestational duration and spontaneous preterm birth. N. Engl. J. Med. 377, 1156–1167 (2017).
pubmed: 28877031
pmcid: 5561422
doi: 10.1056/NEJMoa1612665
Sole-Navais, P. et al. Genetic effects on the timing of parturition and links to fetal birth weight. Nat. Genet. 55, 559–567 (2023).
Moen, G. H., Hemani, G., Warrington, N. M. & Evans, D. M. Calculating power to detect maternal and offspring genetic effects in Genetic Association Studies. Behav. Genet. 49, 327–339 (2019).
pubmed: 30600410
doi: 10.1007/s10519-018-9944-9
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
pubmed: 26414676
pmcid: 4797329
doi: 10.1038/ng.3406
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).
pubmed: 29292387
pmcid: 5805593
doi: 10.1038/s41588-017-0009-4
Demange, P. A. et al. Estimating effects of parents’ cognitive and non-cognitive skills on offspring education using polygenic scores. Nat. Commun. 13, 4801 (2022).
pubmed: 35999215
pmcid: 9399113
doi: 10.1038/s41467-022-32003-x
Howe, L. J. et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat. Genet. 54, 581–592 (2022).
pubmed: 35534559
pmcid: 9110300
doi: 10.1038/s41588-022-01062-7
Hwang, L. D., Moen, G. H. & Evans, D. M. Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes. Elife 11, e73671 (2022).
Cuellar-Partida, G. et al. Complex-Traits Genetics Virtual Lab: a community-driven web platform for post-GWAS analyses. Bioarchiv https://doi.org/10.1101/518027 (2019).
doi: 10.1101/518027
Kang, H. S., Beak, J. Y., Kim, Y. S., Herbert, R. & Jetten, A. M. Glis3 is associated with primary cilia and Wwtr1/TAZ and implicated in polycystic kidney disease. Mol. Cell Biol. 29, 2556–2569 (2009).
pubmed: 19273592
pmcid: 2682055
doi: 10.1128/MCB.01620-08
Senee, V. et al. Mutations in GLIS3 are responsible for a rare syndrome with neonatal diabetes mellitus and congenital hypothyroidism. Nat. Genet. 38, 682–687 (2006).
pubmed: 16715098
doi: 10.1038/ng1802
Taha, D., Barbar, M., Kanaan, H. & Williamson Balfe, J. Neonatal diabetes mellitus, congenital hypothyroidism, hepatic fibrosis, polycystic kidneys, and congenital glaucoma: a new autosomal recessive syndrome? Am. J. Med. Genet. A 122A, 269–273 (2003).
pubmed: 12966531
doi: 10.1002/ajmg.a.20267
Dimitri, P. et al. Novel GLIS3 mutations demonstrate an extended multisystem phenotype. Eur. J. Endocrinol. 164, 437–443 (2011).
pubmed: 21139041
doi: 10.1530/EJE-10-0893
Dou, H. Y. et al. Association between genetic variants and characteristic symptoms of type 2 diabetes: A matched case-control study. Chin. J. Integr. Med. 23, 415–424 (2017).
pubmed: 26919830
doi: 10.1007/s11655-015-2290-3
Hu, C. et al. Variants from GIPR, TCF7L2, DGKB, MADD, CRY2, GLIS3, PROX1, SLC30A8 and IGF1 are associated with glucose metabolism in the Chinese. PLoS ONE 5, e15542 (2010).
pubmed: 21103350
pmcid: 2984505
doi: 10.1371/journal.pone.0015542
Liu, C. et al. Variants in GLIS3 and CRY2 are associated with type 2 diabetes and impaired fasting glucose in Chinese Hans. PLoS ONE 6, e21464 (2011).
pubmed: 21747906
pmcid: 3126830
doi: 10.1371/journal.pone.0021464
Miranda-Lora, A. L. et al. Genetic polymorphisms associated with pediatric-onset type 2 diabetes: a family-based transmission disequilibrium test and case-control study. Pediatr. Diab. 20, 239–245 (2019).
doi: 10.1111/pedi.12818
Rees, S. D. et al. Effects of 16 genetic variants on fasting glucose and type 2 diabetes in South Asians: ADCY5 and GLIS3 variants may predispose to type 2 diabetes. PLoS ONE 6, e24710 (2011).
pubmed: 21949744
pmcid: 3176767
doi: 10.1371/journal.pone.0024710
Inshaw, J. R. J. et al. Analysis of overlapping genetic association in type 1 and type 2 diabetes. Diabetologia 64, 1342–1347 (2021).
pubmed: 33830302
pmcid: 8099827
doi: 10.1007/s00125-021-05428-0
Barker, A. et al. Association of genetic Loci with glucose levels in childhood and adolescence: a meta-analysis of over 6,000 children. Diabetes 60, 1805–1812 (2011).
pubmed: 21515849
pmcid: 3114379
doi: 10.2337/db10-1575
Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).
pubmed: 20081858
pmcid: 3018764
doi: 10.1038/ng.520
Boesgaard, T. W. et al. Variants at DGKB/TMEM195, ADRA2A, GLIS3 and C2CD4B loci are associated with reduced glucose-stimulated beta cell function in middle-aged Danish people. Diabetologia 53, 1647–1655 (2010).
pubmed: 20419449
doi: 10.1007/s00125-010-1753-5
Hong, K. W., Chung, M. & Cho, S. B. Meta-analysis of genome-wide association study of homeostasis model assessment beta cell function and insulin resistance in an East Asian population and the European results. Mol. Genet. Genomics 289, 1247–1255 (2014).
pubmed: 25073516
doi: 10.1007/s00438-014-0885-6
Hattersley, A. T. & Tooke, J. E. The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet 353, 1789–1792 (1999).
pubmed: 10348008
doi: 10.1016/S0140-6736(98)07546-1
Li, E., Bestor, T. H. & Jaenisch, R. Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 69, 915–926 (1992).
pubmed: 1606615
doi: 10.1016/0092-8674(92)90611-F
Winkelmann, J. et al. Mutations in DNMT1 cause autosomal dominant cerebellar ataxia, deafness and narcolepsy. Hum. Mol. Genet. 21, 2205–2210 (2012).
pubmed: 22328086
pmcid: 3465691
doi: 10.1093/hmg/dds035
Klein, C. J. et al. DNMT1 mutation hot spot causes varied phenotypes of HSAN1 with dementia and hearing loss. Neurology 80, 824–828 (2013).
pubmed: 23365052
pmcid: 3598458
doi: 10.1212/WNL.0b013e318284076d
Klein, C. J. et al. Mutations in DNMT1 cause hereditary sensory neuropathy with dementia and hearing loss. Nat. Genet. 43, 595–600 (2011).
pubmed: 21532572
pmcid: 3102765
doi: 10.1038/ng.830
Hirasawa, R. et al. Maternal and zygotic Dnmt1 are necessary and sufficient for the maintenance of DNA methylation imprints during preimplantation development. Genes Dev. 22, 1607–1616 (2008).
pubmed: 18559477
pmcid: 2428059
doi: 10.1101/gad.1667008
Solé-Navais, P. et al. Genetic effects on the timing of parturition and links to fetal birth weight. Nat Genet. 55, 559–567 (2023).
Beaumont, R. N. et al. Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth. Nat Genet. 55, 1807–1819 (2023).
Wu, Y. et al. Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies. Proc. Natl Acad. Sci. USA 118, e2023184118 (2021).
Warrington, N. M., Hwang, L. D., Nivard, M. G. & Evans, D. M. Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data. Nat. Commun. 12, 5420 (2021).
pubmed: 34521848
pmcid: 8440517
doi: 10.1038/s41467-021-25723-z
Gray, K. J., Saxena, R. & Karumanchi, S. A. Genetic predisposition to preeclampsia is conferred by fetal DNA variants near FLT1, a gene involved in the regulation of angiogenesis. Am. J. Obstet. Gynecol. 218, 211–218 (2018).
pubmed: 29138037
doi: 10.1016/j.ajog.2017.11.562
Steinthorsdottir, V. et al. Genetic predisposition to hypertension is associated with preeclampsia in European and Central Asian women. Nat. Commun. 11, 5976 (2020).
pubmed: 33239696
pmcid: 7688949
doi: 10.1038/s41467-020-19733-6
Gillett, A. C., Vassos, E. & Lewis, C. M. Transforming summary statistics from logistic regression to the liability scale: application to genetic and Environmental Risk Scores. Hum. Hered. 83, 210–224 (2018).
pubmed: 30865946
doi: 10.1159/000495697
Pawitan, Y., Seng, K. C. & Magnusson, P. K. How many genetic variants remain to be discovered? PLoS ONE 4, e7969 (2009).
pubmed: 19956539
pmcid: 2780697
doi: 10.1371/journal.pone.0007969
So, H. C., Gui, A. H., Cherny, S. S. & Sham, P. C. Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases. Genet. Epidemiol. 35, 310–317 (2011).
pubmed: 21374718
doi: 10.1002/gepi.20579
Wu, T. & Sham, P. C. On the Transformation Of Genetic Effect Size From Logit To Liability Scale. Behav. Genet. 51, 215–222 (2021).
pubmed: 33630212
doi: 10.1007/s10519-021-10042-2
de la Fuente, J., Grotzinger, A. D., Marioni, R. E., Nivard, M. G. & Tucker-Drob, E. M. Integrated analysis of direct and proxy genome wide association studies highlights polygenicity of Alzheimer’s disease outside of the APOE region. PLoS Genet. 18, e1010208 (2022).
pubmed: 35658006
pmcid: 9200312
doi: 10.1371/journal.pgen.1010208
Liu, J. Z., Erlich, Y. & Pickrell, J. K. Case-control association mapping by proxy using family history of disease. Nat. Genet. 49, 325–331 (2017).
pubmed: 28092683
doi: 10.1038/ng.3766
Hujoel, M. L. A., Gazal, S., Loh, P. R., Patterson, N. & Price, A. L. Liability threshold modeling of case-control status and family history of disease increases association power. Nat. Genet. 52, 541–547 (2020).
pubmed: 32313248
pmcid: 7210076
doi: 10.1038/s41588-020-0613-6
Li, A. et al. mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data. Am J Hum Genet. 110, 30–43 (2023).
Wang, B. et al. Robust genetic nurture effects on education: a systematic review and meta-analysis based on 38,654 families across 8 cohorts. Am. J. Hum. Genet. 108, 1780–1791 (2021).
pubmed: 34416156
pmcid: 8456157
doi: 10.1016/j.ajhg.2021.07.010
Morris, J. A. et al. An atlas of genetic influences on osteoporosis in humans and mice. Nat. Genet. 51, 258–266 (2019).
pubmed: 30598549
doi: 10.1038/s41588-018-0302-x
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
pubmed: 29184056
pmcid: 5705698
doi: 10.1038/s41467-017-01261-5
Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).
pubmed: 36653562
pmcid: 9849126
doi: 10.1038/s41586-022-05473-8