GWAS advancements to investigate disease associations and biological mechanisms

Oluwaferanmi Omidiran , Aashna Patel , Sarah Usman , Ishani Mhatre , Habiba Abdelhalim , William DeGroat , Rishabh Narayanan , Kritika Singh , Dinesh Mendhe , Zeeshan Ahmed

Clinical and Translational Discovery ›› 2024, Vol. 4 ›› Issue (3) : e296

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Clinical and Translational Discovery ›› 2024, Vol. 4 ›› Issue (3) :e296 DOI: 10.1002/ctd2.296
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GWAS advancements to investigate disease associations and biological mechanisms

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Abstract

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations and pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including whole-genome sequencing (WGS), Mendelian randomisation (MR), the Pangenome's high-quality Telomere-to-Telomere (T2T)-CHM13 panel and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. The state of the literature demonstrates the capabilities of these techniques to enhance the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined single nucleotide polymorphism sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications. MR has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques such as HuBMAP enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aimed to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

Keywords

GWAS / HuBMAP / Mendelian randomisation / Pangenome / whole-genome sequencing

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Oluwaferanmi Omidiran, Aashna Patel, Sarah Usman, Ishani Mhatre, Habiba Abdelhalim, William DeGroat, Rishabh Narayanan, Kritika Singh, Dinesh Mendhe, Zeeshan Ahmed. GWAS advancements to investigate disease associations and biological mechanisms. Clinical and Translational Discovery, 2024, 4(3): e296 DOI:10.1002/ctd2.296

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References

[1]

UffelmannE, HuangQQ, MunungNS, et al. Genome-wide association studies. Nat Rev Methods Primers. 2021;1(1):59.

[2]

PadmanabhanS, Dominiczak AF. Genomics of hypertension: the road to precision medicine. Nat Rev Cardiol. 2021;18(4):235-250.

[3]

OzakiK, Ohnishi Y, IidaA, et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002;32(4):650-654.

[4]

CoxD. The mystery of our genome's dark matter. BBC News. 2023.

[5]

MakowskyR, Pajewski NM, KlimentidisYC, et al. Beyond missing heritability: prediction of complex traits. PLoS Genet. 2011;7(4):e1002051.

[6]

YunusbaevU, ValeevA, YunusbaevaM, et al. Reconstructing recent population history while mapping rare variants using haplotypes. Sci Rep. 2019;9(1):5849.

[7]

ReichD, Patterson N, CampbellD, et al. Reconstructing native American population history. Nature. 2012;488(7411):370-374.

[8]

HomerN, Szelinger S, RedmanM, et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 2008;4(8):e1000167.

[9]

TreffNR, SuJ, TaoX, MillerKA, LevyB. A novel single-cell DNA fingerprinting method successfully distinguishes sibling human embryos. Fertil Steril. 2010;94(2):477-484.

[10]

WalshR, Jurgens SJ, ErdmannJ, BezzinaCR. Genome-wide association studies of cardiovascular disease. Physiol Rev. 2023;103(3):2039-2055.

[11]

LanderES, LintonLM, BirrenB, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860-921.

[12]

ChenX, SunYC, ChurchGM, Lee JH, ZadorAM. Efficient in situ barcode sequencing using padlock probe-based BaristaSeq. Nucleic Acids Res. 2018;46(4):e22.

[13]

CrossleyBM, BaiJ, GlaserA, et al. Guidelines for Sanger sequencing and molecular assay monitoring. J Vet Diagn Invest. 2020;32(6):767-775.

[14]

HuT, Chitnis N, MonosD, DinhA. Next-generation sequencing technologies: an overview. Hum Immunol. 2021;82(11):801-811.

[15]

JelinAC, VoraN. Whole exome sequencing: applications in prenatal genetics. Obstet Gynecol Clin North Am. 2018;45(1):69-81.

[16]

LouhelainenJ. SNP arrays. Microarrays. 2016;5(4):27.

[17]

HöglundJ, RafatiN, Rask-AndersenM, et al. Improved power and precision with whole genome sequencing data in genome-wide association studies of inflammatory biomarkers. Sci Rep. 2019;9(1):16844.

[18]

SlovinS, Carissimo A, PanarielloF, et al. Single-cell RNA sequencing analysis: a step-by-step overview. Methods Mol Biol. 2021;2284:343-365.

[19]

ShiY, WangG, LauHC, Yu J. Metagenomic sequencing for microbial DNA in human samples: emerging technological advances. Int J Mol Sci. 2022;23(4):2181.

[20]

TamV, PatelN, TurcotteM, Bossé Y, ParéG, MeyreD. Benefits and limitations of genome-wide association studies. Nat Rev Genet. 2019;20(8):467-484.

[21]

Sinclair-WatersM, Ødegård J, KorsvollSA, et al. Beyond large-effect loci: large-scale GWAS reveals a mixed large-effect and polygenic architecture for age at maturity of Atlantic salmon. Genet Sel Evol. 2020;52(1):9.

[22]

YouQ, YangX, PengZ, Xu L, WangJ. Development and applications of a high throughput genotyping tool for polyploid crops: single nucleotide polymorphism (SNP) array. Front Plant Sci. 2018;9:104.

[23]

GeibelJ, ReimerC, WeigendS, Weigend A, PookT, SimianerH. How array design creates SNP ascertainment bias. PLoS One. 2021;16(3):e0245178.

[24]

KierczakM, RafatiN, HöglundJ, et al. Contribution of rare whole-genome sequencing variants to plasma protein levels and the missing heritability. Nat Commun. 2022;13(1):2532.

[25]

SatamH, JoshiK, MangroliaU, et al. Next-generation sequencing technology: current trends and advancements. Biology. 2023;12(7):997.

[26]

CeballosFC, Hazelhurst S, RamsayM. Assessing runs of homozygosity: a comparison of SNP array and whole genome sequence low coverage data. BMC Genomics. 2018;19(1):106.

[27]

NakagawaH, FujitaM. Whole genome sequencing analysis for cancer genomics and precision medicine. Cancer Sci. 2018;109(3):513-522.

[28]

MeggendorferM, Jobanputra V, WrzeszczynskiKO, et al. Analytical demands to use whole-genome sequencing in precision oncology. Semin Cancer Biol. 2022;84:16-22.

[29]

GoodwinS, McPherson JD, McCombieWR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet. 2016;17(6):333-351.

[30]

MurrayJ. The “All of Us” research program. N Engl J Med. 2019;381(19):1884.

[31]

DoerrM, MooreS, BaroneV, et al. Assessment of the All of Us research program's informed consent process. AJOB Empir Bioeth. 2021;12(2):72-83.

[32]

Byrska-BishopM, EvaniUS, ZhaoX, et al. High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios. Cell. 2022;185(18):3426-3440.e19.

[33]

HanchardNA, Choudhury A. 1000 Genomes Project phase 4: the gift that keeps on giving. Cell. 2022;185(18):3286-3289.

[34]

SudlowC, Gallacher J, AllenN, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.

[35]

SimsD, Sudbery I, IlottNE, HegerA, Ponting CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014;15(2):121-132.

[36]

ChenW, Coombes BJ, LarsonNB. Recent advances and challenges of rare variant association analysis in the biobank sequencing era. Front Genet. 2022;13:1014947.

[37]

KvapilovaK, Misenko P, RadvanszkyJ, et al. Validated WGS and WES protocols proved saliva-derived gDNA as an equivalent to blood-derived gDNA for clinical and population genomic analyses. BMC Genom. 2024;25(1):187.

[38]

FittallMW, Van Loo P. Translating insights into tumor evolution to clinical practice: promises and challenges. Genom Med. 2019;11(1):20.

[39]

McGranahanN, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168(4):613-628.

[40]

MolnarMJ, KovacsGG. Mitochondrial diseases. Handb Clin Neurol. 2017;145:147-155.

[41]

XuJ, MaoC, HouY, et al. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Cell Rep. 2022;41(9):111717.

[42]

ChenJ, RuanX, SunY, et al. Multi-omic insight into the molecular networks of mitochondrial dysfunction in the pathogenesis of inflammatory bowel disease. EBioMedicine. 2024;99:104934.

[43]

RoselliC, Chaffin MD, WengLC, et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat Genet. 2018;50(9):1225-1233.

[44]

NielsenJB, Thorolfsdottir RB, FritscheLG, et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet. 2018;50(9):1234-1239.

[45]

GudbjartssonDF, Helgason H, GudjonssonSA, et al. Large-scale whole-genome sequencing of the Icelandic population. Nat Genet. 2015;47(5):435-444.

[46]

MartinsILF, Almeida FVDS, SouzaKP, BritoFCF, Rodrigues GD, ScaramelloCBV. Reviewing atrial fibrillation pathophysiology from a network medicine perspective: the relevance of structural remodeling, inflammation, and the immune system. Life. 2023;13(6):1364.

[47]

MarkidesV, Schilling RJ. Atrial fibrillation: classification, pathophysiology, mechanisms and drug treatment. Heart. 2003;89(8):939-943.

[48]

ShigemizuD, Asanomi Y, AkiyamaS, MitsumoriR, NiidaS, OzakiK. Whole-genome sequencing reveals novel ethnicity-specific rare variants associated with Alzheimer's disease. Mol Psychiatry. 2022;27(5):2554-2562.

[49]

ZhouX, Feliciano P, ShuC, et al. Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes. Nat Genet. 2022;54(9):1305-1319.

[50]

ZhouJ, ParkCY, TheesfeldCL, et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet. 2019;51(6):973-980.

[51]

ZahirFR, Mwenifumbo JC, ChunHE, et al. Comprehensive whole genome sequence analyses yields novel genetic and structural insights for intellectual disability. BMC Genom. 2017;18(1):403.

[52]

ZhaoEY, JonesM, JonesSJM. Whole-genome sequencing in cancer. Cold Spring Harb Perspect Med. 2019;9(3):a034579.

[53]

MittM, KalsM, PärnK, et al. Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel. Eur J Human Genet. 2017;25(7):869-876.

[54]

LeeEY, MakACY, HuD, et al. Whole-genome sequencing identifies novel functional loci associated with lung function in puerto rican youth. Am J Respir Crit Care Med. 2020;202(7):962-972.

[55]

MalcherA, Stokowy T, BermanA, et al. Whole-genome sequencing identifies new candidate genes for nonobstructive azoospermia. Andrology. 2022;10(8):1605-1624.

[56]

RyanSL, PedenJF, KingsburyZ, et al. Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia. Leukemia. 2023;37(3):518-528.

[57]

BoehmFJ, ZhouX. Statistical methods for Mendelian randomization in genome-wide association studies: a review. Comput Struct Biotechnol J. 2022;20:2338-2351.

[58]

BurgessS, SmallDS, ThompsonSG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26(5):2333-2355.

[59]

SawaT. The exact sampling distribution of ordinary least squares and two-stage least squares estimators. J Am Statist Assoc. 1969;64(327):923-937.

[60]

BurgessS, Butterworth A, ThompsonSG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658-665.

[61]

MorrisonJ, Knoblauch N, MarcusJH, StephensM, HeX. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020;52(7):740-747.

[62]

BowdenJ, Davey Smith G, BurgessS. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512-525.

[63]

FangS, HemaniG, RichardsonTG, GauntTR, Davey Smith G. Evaluating and implementing block jackknife resampling Mendelian randomization to mitigate bias induced by overlapping samples. Hum Mol Genet. 2023;32(2):192-203.

[64]

van DijkPJ, JessopAP, EllisTHN. How did Mendel arrive at his discoveries? Nat Genet. 2022;54(7):926-933.

[65]

BurgessS, MasonAM, GrantAJ, et al. Using genetic association data to guide drug discovery and development: review of methods and applications. Am J Hum Genet. 2023;110(2):195-214.

[66]

Davey SmithG, HemaniG. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89-R98.

[67]

McClellanJ, KingMC. Genetic heterogeneity in human disease. Cell. 2010;141(2):210-217.

[68]

ZhouY, ZhaL, PanS. The risk of atrial fibrillation increases with earlier onset of obesity: a mendelian randomization study. Int J Med Sci. 2022;19(9):1388-1398.

[69]

ChenW, CaiX, YanH, PanY. Causal effect of obstructive sleep apnea on atrial fibrillation: a Mendelian randomization study. J Am Heart Assoc. 2021;10(23):e022560.

[70]

LevinMG, JudyR, GillD, et al. Genetics of height and risk of atrial fibrillation: a Mendelian randomization study. PLoS Med. 2020;17(10):e1003288.

[71]

GajendragadkarPR, Von Ende A, IbrahimM, et al. Assessment of the causal relevance of ECG parameters for risk of atrial fibrillation: a mendelian randomisation study. PLoS Med. 2021;18(5):e1003572.

[72]

MainaJG, Balkhiyarova Z, NouwenA, et al. Bidirectional Mendelian randomization and multiphenotype GWAS show causality and shared pathophysiology between depression and type 2 diabetes. Diabetes Care. 2023;46(9):1707-1714.

[73]

GuoHY, WangW, PengH, Yuan H. Bidirectional two-sample Mendelian randomization study of causality between rheumatoid arthritis and myocardial infarction. Front Immunol. 2022;13:1017444.

[74]

ZhangZ, LiL, HuZ, et al. Causal effects between atrial fibrillation and heart failure: evidence from a bidirectional Mendelian randomization study. BMC Med Genet. 2023;16(1):187.

[75]

SanthanakrishnanR, Wang N, LarsonMG, et al. Atrial fibrillation begets heart failure and vice versa: temporal associations and differences in preserved versus reduced ejection fraction. Circulation. 2016;133(5):484-492.

[76]

WangTJ, LarsonMG, LevyD, et al. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham heart study. Circulation. 2003;107(23):2920-2925.

[77]

QiG, Chatterjee N. Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects. Nat Commun. 2019;10(1):1941.

[78]

WangL, GaoB, FanY, XueF, ZhouX. Mendelian randomization under the omnigenic architecture. Briefings Bioinf. 2021;22(6):bbab322.

[79]

BucurIG, Claassen T, HeskesT. Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach. Stat Methods Med Res. 2020;29(4):1081-1111.

[80]

FarhudDD. Impact of lifestyle on health. Iran J Public Health. 2015;44(11):1442-1444.

[81]

BillingsleyKJ, DingJ, JerezPA, et al. Genome-wide analysis of structural variants in Parkinson disease. Ann Neurol. 2023;93(5):1012-1022.

[82]

KaurS, AliA, AhmadU, Siahbalaei Y, PandeyAK, SinghB. Role of single nucleotide polymorphisms (SNPs) in common migraine. Egypt J Neurol Psychiatry Neurosurg. 2019;55(1):1-7.

[83]

HerreraRJ, Garcia-Bertrand R. Ancestral DNA, Human Origins, and Migrations. Academic Press; 2018.

[84]

VollgerMR, Dishuck PC, HarveyWT, et al. Increased mutation and gene conversion within human segmental duplications. Nature. 2023;617(7960):325-334.

[85]

VollgerMR, Guitart X, DishuckPC, et al. Segmental duplications and their variation in a complete human genome. Science. 2022;376(6588):eabj6965.

[86]

DumontBL. Interlocus gene conversion explains at least 2.7% of single nucleotide variants in human segmental duplications. BMC Genom. 2015;16(1):456.

[87]

EblerJ, EbertP, ClarkeWE, et al. Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes. Nat Genet. 2022;54(4):518-525.

[88]

AbondioP, CilliE, LuiselliD. Human Pangenomics: promises and challenges of a distributed genomic reference. Life. 2023;13(6):1360.

[89]

LiaoWW, AsriM, EblerJ, et al. A draft human pangenome reference. Nature. 2023;617(7960):312-324.

[90]

DobinA, DavisCA, SchlesingerF, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21.

[91]

SibbesenJA, Eizenga JM, NovakAM, et al. Haplotype-aware pantranscriptome analyses using spliced pangenome graphs. Nat Methods. 2023;20(2):239-247.

[92]

U.S. Department of Health and Human Services. The Human Biomolecular Atlas Program (HuBMAP). National Institutes of Health.

[93]

StephensonRS, Atkinson A, KottasP, et al. High resolution 3-Dimensional imaging of the human cardiac conduction system from microanatomy to mathematical modeling. Sci Rep. 2017;7(1):7188.

[94]

HuBMAP Consortium. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature. 2019;574(7777):187-192.

[95]

LeonardHL, NallsMA, Day-WilliamsA, et al. Open science in precision medicine for neurodegenerative diseases. Nat Rev Drug Discov. 2024;23(4):233-234.

[96]

LeeCR, LuzumJA, SangkuhlK, et al. Clinical pharmacogenetics implementation consortium guideline for CYP2C19 genotype and clopidogrel therapy: 2022 update. Clin Pharmacol Ther. 2022;112(5):959-967.

[97]

DeanL. Prasugrel therapy and CYP genotype. In: PrattVM, ScottSA, PirmohamedM, Esquivel B, KattmanBL, MalheiroAJ, Eds. Medical Genetics Summaries. Bethesda, MD, USA: National Center for Biotechnology Information; 2017.

[98]

SeowWJ, MatsuoK, HsiungCA, et al. Association between GWAS-identified lung adenocarcinoma susceptibility loci and EGFR mutations in never-smoking Asian women, and comparison with findings from Western populations. Hum Mol Genet. 2017;26(2):454-465.

[99]

BögerCA, GorskiM, LiM, et al. Association of eGFR-related loci identified by GWAS with incident CKD and ESRD. PLoS Genet. 2011;7(9):e1002292.

[100]

FaraoniI, Graziani G. Role of BRCA mutations in cancer treatment with poly(ADP-ribose) polymerase (PARP) inhibitors. Cancers. 2018;10(12):487.

[101]

Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609-615.

[102]

AkiyamaM. Multi-omics study for interpretation of genome-wide association study. J Hum Genet. 2021;66(1):3-10.

[103]

RotimiCN, Adeyemo AA. From one human genome to a complex tapestry of ancestry. Nature. 2021;590(7845):220-221.

[104]

LiuL, ZhangD, LiuH, ArendtC. Robust methods for population stratification in genome wide association studies. BMC Bioinf. 2013;14:132.

[105]

ZhaoH, MitraN, KanetskyPA, Nathanson KL, RebbeckTR. A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS). Stat Appl Genet Mol Biol. 2018;17(6):/j/sagmb.2018.17.issue-6/sagmb-2017-0054/sagmb-2017-0054.xml.

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