Abstract
The last decade has witnessed an explosion in the depth, variety, and amount of human genetic data that can be generated. This revolution in technical and analytical capacities has enabled the genetic investigation of human traits and disease in thousands to now millions of participants. Investigators have taken advantage of these advancements to gain insight into platelet biology and the platelet’s role in human disease. To do so, large human genetics studies have examined the association of genetic variation with two quantitative traits measured in many population and patient based cohorts: platelet count (PLT) and mean platelet volume (MPV). This article will review the many human genetic strategies—ranging from genome-wide association study (GWAS), Exomechip, whole exome sequencing (WES), to whole genome sequencing (WGS)—employed to identify genes and variants that contribute to platelet traits. Additionally, we will discuss how these investigations have examined and interpreted the functional implications of these newly identified genetic factors and whether they also impart risk to human disease. The depth and size of genetic, phenotypic, and other -omic data are primed to continue their growth in the coming years and provide unprecedented opportunities to gain critical insights into platelet biology and how platelets contribute to disease.
Platelets are circulating anucleate cells important in wound healing, thrombosis, hemostasis, and inflammation. Clinically, platelets contribute to severe bleeding and thrombotic disorders like idiopathic thrombocytopenic purpura and essential thrombocythemia as well as cardiovascular disease including myocardial infarction and atherosclerosis1. In fact, platelets are among the most common targets in secondary prevention of cardiovascular disease via aspirin use and dual anti-platelet therapies2;3. Derived from multinucleate megakaryocytes in bone marrow, a balance of platelet production, maintenance in the circulation, and ultimate clearance from the blood is necessary to ensure proper function.
In clinical and research settings, measuring the number of circulating platelets—platelet count (PLT)—and the average size of circulating platelets—mean platelet volume (MPV)—can reveal insights into patient health as well as the underlying mechanisms of platelet biology and regulation. More practically, measuring PLT and MPV can be completed in a relatively inexpensive and high-throughput manner and is more commonly ascertained than other platelet indices such as plateletcrit, platelet distribution width, and platelet reactivity or other measures of platelet function. Many have also proposed MPV as a more convenient proxy for measuring platelet function1;4. However, it is important to note that considerable variation in MPV can be due to pre-analytic factors including blood tube selection, processing and equipment used5–7. Platelets recently released new editorial guidelines calling for careful consideration of such factors in new studies undertaken and submitted that involve MPV8. Measuring PLT is more standardized but can also face technical issues, particularly for PLT well outside normal ranges such as in extreme thrombocytopenia. Despite these potential limitations, the ability to generate data on both PLT and MPV on large numbers of individuals in various settings has made them attractive candidates to analyse in human studies of platelets.
With the use of PLT and MPV, many have sought to identify their environmental and genetic determinants. PLT and MPV have a strong negative correlation, with fewer number of platelets correlated with larger platelets and vice versa4. There are notable gender, age, and population differences in PLT and MPV, with women generally having higher PLT and PLT decreasing with age9–11. However, heritability studies in twin and family-based cohorts have demonstrated PLT and MPV also have strong genetic components12–14. Further supporting the importance of genetic effects, causative genes of several inherited thrombocytopenia (PLT < 150,000 platelets/µL), thrombocytosis (PLT > 450,000 platelets/µL), and platelet function disorders have been identified and characterized. For example, variants in the WAS gene cause Wiskott-Aldrich syndrome (OMIM #301000), a rare X-linked disorder characterized by eczema, thrombocytopenia, and infection15. Identification of causal genes and variants of rare platelet disorders improves proper diagnosis and can aid in understanding underlying pathobiology. As the goal of this review is to discuss the genetics of PLT and MPV in the general population, please see recent reviews for fuller discussion of the genetics of inherited platelet disorders16;17.
While the benefits of identifying genetic variants that cause platelet disorders are intuitive, the purposes of characterizing genes contributing to inter-individual variability in PLT and MPV may be less so. However, human genetic association strategies can identify factors that play critical roles in platelet biology and inform experimental dissection of these pathways. Fully and appropriately leveraging genetic associations has the potential to disentangle the complicated regulation of platelets and to give insights of platelets’ contribution to chronic and acute conditions.
To identify genetic factors that influence PLT and MPV in the general population, investigators have employed diverse approaches (Table 1). One of the first studies took advantage of familial relatedness in a genome-wide linkage scan to identify a suggestive linkage peak on chromosome 19q13.13–19q13.31 for PLT and hypothesized that glycoprotein VI (GP6)—a platelet receptor for collagen—was responsible for this association18. However, the genome-wide association study (GWAS) is now by far the most widely used strategy to identify PLT and MPV genetic associations (Table 1). In GWAS, investigators genotype hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) designed to tag genomic variation and assess the association of each SNP with a trait19. GWAS can identify genomic regions where common variants (i.e., minor allele frequency [MAF] > 5%) influence traits. Individual common variants typically have small, yet critical regulatory effects. Taken together, common variants can have substantial effect on phenotypes and reflect the large number of such variants associated with complex traits like PLT and MPV20. Disentangling these effects can reveal key players in how platelet traits are regulated and the genetic architecture underlying PLT and MPV.
Table 1:
Method | Abbreviation | Description | Type of variation | Studies |
---|---|---|---|---|
Genome-wide association study |
GWAS | Genotyping of 100,000s to millions of common single nucleotide polymorphisms (SNPs) across the genome to identify regions associated with trait of interest |
Common Non-coding Regulatory |
Meisinger et al. 2009 PMID: 19110211 Soranzo et al. 2009 PMID: 19221038 Soranzo et al. 2009 PMID: 19820697 Gieger et al. 2011 PMID: 22139419 Schick et al. 2016 PMID: 26805783 Qayyum et al. 2012 PMID: 22423221 Li et al. 2013 PMID: 23263863 Oh et al. 2014 PMID: 25705162 Kim et al. 2015 PMID: 26064965 Kamatani et al. 2010 PMID: 20139978 Shameer et al. 2014 PMID: 24026423 Astle et al. 2016 PMID: 27863252 |
Whole exome sequencing |
WES | Direct sequencing of coding regions of the genome to identify non-synonymous and synonymous variants associated with trait of interest |
Rare Coding |
Auer et al. 2012 PMID: 23103231 Polfus et al. 2016 PMID: 27486782 |
Exomechip | Genotyping of ~250,000 previously observed rare to low frequency coding variants and a small fraction of previously associated SNPs from GWAS to examine contribution of exonic variants to trait of interest and to replicate previous findings, respectively |
Mostly Rare Mostly Coding |
Auer et al. 2014 PMID: 24777453 Eicher et al. 2016 PMID: 27346686 |
|
Whole genome sequencing |
WGS | Direct sequencing of the entire genome to identify individual SNPs, genomic regions, or other types of variation associated with trait of interest. Sometimes such data are used as a reference panel to impute or infer genotypes in larger samples. |
All | Iotchkova et al. 2016 PMID: 27668658 |
Phenome-wide association study |
PheWAS | Examining the association of a single genetic variant across hundreds of available traits to determine possible effects of a variant across phenotypes |
Usually common | Shameer et al. 2014 PMID: 24026423 |
Mendelian Randomization |
MR | Using the random assortment of genetic alleles and SNPs as strong instrument variables for prospective intermediate phenotypes to determine causal relationships between different traits |
N/A | Astle et al. 2016 PMID: 27863252 |
In 2009, the first platelet GWASs identified 4 genomic loci (WDR66, ARHGEF3, TAOK1, PIK3CG) associated with MPV21;22. Later that year, investigators in six European ancestry population-based cohorts formed the HaemGen Consortium23. In addition to confirming the four previously identified loci, meta-analysing GWAS results (discovery n=4,627, replication n=9,316) identified 12 additional loci associated with PLT and/or MPV. This collaboration substantially increased sample size and thus statistical power to detect the small effects typical of common variants identified through GWAS. Additionally, the investigators built the collaborative and analytical framework to integrate GWAS results from independent studies. With this framework established, more groups joined to further increase discovery GWAS sample size (PLT n=48,666, MPV=18,600)24. Doing so enabled the identification of 68 loci—52 not previously reported—associated with PLT and/or MPV. Of these 68 loci, 16 had genome-wide significant associations with both PLT and MPV (p<5.0×10−8), indicative of their strong inverse phenotypic correlation4. The associated genetic factors explained 4.8% and 9.9% of the PLT and MPV phenotypic variance, respectively, indicating that common variants substantially contribute to these traits.
Although Gieger et al. further characterized their associations in samples of non-European ancestry, PLT and MPV discovery GWASs presented thus far were completed only in subjects of European ancestry. Epidemiologically, PLT and MPV differ among ethnic and population groups10. Such differences may indicate possible ethnic and population differences in genetic architecture and the specific SNPs associated with each. To begin to address this, several GWASs have been performed in samples of African, Hispanic, and Asian ancestries. Doing so has not only confirmed many associations observed in European ancestry cohorts, but also identified new loci associated with PLT and MPV. For example, a recent GWAS of PLT in the Hispanic Community Health Study/Study of Latinos (discovery n=12,491) identified 5 novel associations25, at least partially due to differing MAFs of associated SNPs in ACTN1, ETV7, and MEF2C. These allele frequency differences enabled detection of effects that were not observed in more well-powered GWASs in European ancestry samples. Similarly, GWASs in Asian and African ancestry samples have identified novel associations including KIAA0232, SLMO2, ACAD10, and EGF26–30. Several of these SNP associations (e.g., EGF, SLMO2) were subsequently detected in other populations, while others with more substantial allele frequency differences have not been observed (e.g., KIAA0232). GWASs in samples of non-European ancestry have identified novel genetic associations and demonstrated population similarities and differences in genetic associations. A larger investment and continued effort in studying platelet traits in non-European ancestry groups are necessary to disentangle ethnic-specific and ethnic-independent genetic effects.
Not only is there a need to further increase sample diversity, but the need to increase sample size and depth of data collected continues to grow. Despite the on-going challenge to translate genetic associations into clinical or biological mechanism, increasing sample size, diversity, and depth offers the opportunity to refine the implications of genetic associations into contributing pathways. Building upon these studies will further inform the necessary experiments to interrogate associated loci and pathways both at the bench and in the clinic. The emergence of biobank and electronic health record (EHR) cohorts epitomizes the mobilization of mass resources to fuel genetic research. An example of utilizing EHR-based cohorts is the recent study of PLT and MPV in the Electronic Medical Records and Genomics (eMERGE) network. Although all observed PLT and MPV associations had been previously reported, the use of EHRs enabled investigators to perform a phenome-wide association study (PheWAS) of associated SNPs. By doing so, they examined the relationships of PLT/MPV associated SNPs across all measured phenotypes in the EHR system and observed associations with myocardial infarction and auto-immune disease31. Such a PheWAS strategy can tie genetic associations of quantitative traits like PLT and MPV with human disease. The largest reported GWAS of PLT and MPV (n=166,066) at the time of this review took advantage of similar EHR and biobank resources in the UK Biobank and INTERVAL studies32. Such a vast resource enabled detection of hundreds of platelet associated loci that explained 18% and 30% of PLT and MPV variation, respectively. As sample size, participant diversity, and phenotype depth increases through national biobank and registry, EHR, and other initiatives, the opportunity to identify genetic factors associated with PLT/MPV and their relationships with clinical traits and disease will continue to grow.
Despite their success in identifying PLT and MPV associated loci, GWAS results do not fully account for their genetic components. As GWAS largely interrogate common variants (i.e., MAF > 5%), the field has further examined the contribution of rare (MAF < 1%) and low-frequency (MAF: 1–5%) variants to population variation in PLT and MPV. One such strategy is to target the protein coding portion of the genome through whole exome sequencing (WES), where exons are specifically sequenced and coding variants examined (Table 1). WES has successfully identified causal genetic variants for several platelet disorders and other rare inherited conditions16;17. However, there is evidence that rare variation also contributes to population variation of complex traits33. These associated rare, coding variants typically have larger effects than common variants examined in GWAS and can offer relatively easier functional interrogation in follow-up experiments.
Two WES population-based studies of PLT have been reported. The first study presented the results of WES in a sample of 761 African-American subjects. Realizing this sample size gave inadequate statistical power to detect associations of rare coding variants, the authors used these exome data to impute genotypes in a larger sample of over 13,000 African-American individuals. With this larger sample size, they found associations of rare variants in BAK1 and MPL as well as suggestive associations with ITGB3, CD109, and CD3634. Another study employed a different strategy by generating WES data on over 15,000 individuals from six population-based cohorts and then performing in silico replication in over 52,000 participants35. There, they found replicated associations of rare variants in GFI1B and CSP1 with PLT. WES studies provided an orthogonal means to GWAS of identifying genomic loci important in platelet traits. Specifically, these studies have identified new genetic factors (e.g., CPS1) and putatively functional variants in known genes (e.g., BAK1, MPL, ITGB3, CD109, CD36, GFI1B), which would have been difficult to detect with GWAS.
With more WES studies completed, the need for a fast and inexpensive method to increase sample size for discovery and replication emerged. With that in mind, a new tool called Exomechip was developed. Enriched with rare coding variants previously observed in WES studies and SNPs identified in previous GWASs, Exomechip allowed for quick genotyping of the same rare variants in large numbers that would have taken years and substantial resources otherwise36. Two investigations used Exomechip to identify novel genetic associations with increased sample sizes of PLT and MPV to over 150,000 and 57,000 individuals, respectively. This expansion of sample size and use of Exomechip enabled the detection of many novel associations, including JAK2, TUBB1, MAP1A, ZMIZ2, PEAR1, and PACSIN237;38. A small proportion of these novel loci contained associated rare variants as intended with the Exomechip design. The relative lack of novel rare variant associations in Exomechip studies may indicate that increased sample size and unbiased genetic data from WES and whole genome sequencing (WGS) platforms may be necessary to detect many such associations.
As sequencing technology has evolved, the prospect of performing WGS in large studies is quickly becoming feasible. Generating WGS data in these cohorts will allow for improved resolution of relevant genomic elements in identified loci, but also direct interrogation of rare non-coding variants. However, before these data are generated in large enough numbers, investigators can use WGS in smaller samples to infer genotypes in other individuals. For example, Iotchkova et al. used WGS data from the UK10K (n=3,781) and 1000 Genomes projects to infer over 17 million variants in approximately 40,000 individuals39. This increase in genomic coverage allowed them to confirm many and detect several novel associations with PLT (e.g., S1PR3, TP53BP1, and TRABC-MOV10L1). As more WGS data are generated, the ability to impute genotypes and to increase resolution of genetic associations will grow, particularly with the recent advent of readily accessible imputation servers (e.g., imputationserver.sph.umich.edu/index.html and imputation.sanger.ac.uk)40;41.
Genetic association studies are typically just a first step in examining the role of PLT/MPV associated genes and variants. Further analytical, animal, and molecular work is necessary to fully examine the biological mechanisms underlying associations. Most simply, cross-referencing genetic association results has shown large overlap among blood cells (i.e., red blood cells, white blood cells, and platelets) as well as between platelets and lipids38 (Figure 1). More formally, genetic correlations using GWAS has confirmed the shared genetic architecture of PLT and MPV, as well as suggested shared genetic factors of platelets with LDL and height42 (Figure 1). Such analyses can reveal relationships that platelets share with seemingly disparate traits. In addition, identifying genetic factors that strongly influence platelet traits can enable other analyses including Mendelian Randomization to assess causal relationships between platelets and other clinical traits43. Mendelian Randomization analyses can tease apart whether platelets causally impact disease and risk factors or whether epidemiological associations are simply correlative. For example, using a polygenic risk score of associated variants, Astle and colleagues examined the causal effects of PLT and MPV on several auto-immune, cardiometabolic, and neuropsychiatric disorders32. They observed a weak inverse relationship between MPV and coronary heart disease (Figure 1). Observational studies of MPV and CHD are mixed, with the largest systematic meta-analysis finding a positive relationship, opposite of what Astle et al. observed using Mendelian Randomization44;45. Though controversies remain regarding the standardized measurement of MPV and its correlation with other platelet and cardiometabolic indices, the use of Mendelian Randomization and selection of proper genetic instruments may present a new strategy to disentangle relationships of platelets with disease46.
Functional insights are more typically ascertained through interrogation of individual variants and genes. Most commonly, associated variants are annotated using gene expression, epigenetic, and evolutionary resources as well as variant prediction tools like CADD, SIFT, and PolyPhen47. Such annotations can give clues to whether an associated variant may be causal and how it exerts its effects. For example, predicted deleterious rare coding variants in evolutionary conserved regions may affect protein function; while variants within enhancers also associated with gene expression likely have regulatory effects. Recent manuscripts have shown many PLT/MPV associated SNPs are also expression (eQTLs) or splicing (sQTLs) quantitative trait loci that influence gene expression and/or isoform of gene expressed32;35;38. Such overlap suggests regulatory effects mediate much of the genetic effects on platelet traits in the general population. Taking advantage of growing resources integrating transcriptomic, epigenomic, proteomic, and metabolomic with genomic data will be vital in translating associations to biological mechanism.
Despite their implicit relevance to clinical outcomes, human-based genetic and -omic studies cannot entirely untangle the effects of associated genes and variants. To specifically interrogate these missing links, a number of functional investigations in animal and cellular models have examined associated genes. A common approach is to knockout or knockdown expression of a candidate gene in model systems. For example, Gieger et al. further examined their GWAS findings by knocking down expression of 15 prioritized genes in the fruitfly D. melanogaster and zebrafish D. rerio. These experiments recapitulated many hematopoietic phenotypes observed in rodents and revealed a degree of conservation in platelet biology24. Other investigations have shown effects of GWAS associated genes (e.g., PEAR1, NFE2, PIK3CG, JMJD1C) on platelet development, maintenance, and clearance in animal and cellular systems48–53. Human genetic studies of platelet reactivity have also indicated a role for some of these loci in affecting platelet aggregation54;55. Together, these studies provide stronger evidence that genes implicated in human association studies do in fact play a role in platelet biology.
In addition to studying the functions of entire genes, other investigations have examined the functional consequences of specific variants and how they may yield their effects. Overlap with reported eQTLs only provides limited evidence that different SNP alleles causally influence gene expression. Experimental evidence functionally linking genetic variation to molecular phenotypes is fundamental to solidifying regulatory effects. In a recent WES study, a PLT association variant (rs150813342) in GFI1B was shown to promote production of a short GFI1B protein isoform as opposed to a long isoform35. Further experiments showed that this long GFI1B isoform promoted megakaryocyte differentiation and platelet production as compared to the short isoform, proposing the molecular mechanism of how a PLT associated SNP could influence platelet number. Similarly, other studies have demonstrated how SNPs can introduce alternate promoters in DNM3 and shown allelic differences in transcription factor binding in ACTN125;56;57. These functional interrogations of regulatory implicated genetic variants are vital to tying statistical associations to biology.
Despite the successes of the above studies, this translation of associated genetic variants to underlying functional mechanism remains a major hurdle. Genetic association studies have revealed shared genetic factors among numerous traits, suggesting common regulatory pathways (Figure 1). Additionally, the accumulation of genetic associations in cytoskeletal-related genes (e.g., ACTN1, PACSIN2) has further confirmed the importance of cytoskeletal dynamics in megakaryocytes with effects on PLT and MPV58;59 . Identification of more associated loci has the prospect of revealing new pathways that contribute to PLT/MPV. However, to confirm the roles of these pathways in PLT and MPV, direct experimentation at the bench is necessary. Only through the communication of genetic association findings and collaborative translation to appropriate experimental systems will the genetic factors that govern PLT and MPV be identified, confirmed, and properly interrogated.
Human genetic studies leveraging large clinical samples and the emergence of high-throughput genomic technologies have enabled the detection of many genetic contributors to PLT and MPV. Increasing the number, depth, and complexity of platelet and genetic data will further reveal more genetic factors that influence platelet traits and their relationships with human disease. As WGS and massive nation-/healthcare-based cohorts grow through programs such as Genomics England, the NIH’s Precision Medicine Initiative, and the VA’s Million Veterans Project, PLT and MPV can serve as model complex traits to refine genetic analyses. Identifying associated genetic factors is only the beginning of identifying the mechanisms that govern platelet biology, their relationships with human disease, and possible platelet therapeutics targets. Connecting PLT/MPV associated genetic factors with biological function is vital in understanding platelet mechanisms and their contributions to human disease. Future studies taking advantage of the single base pair resolution of WGS need to focus on the translation of such association signals to both their functional and clinical implications. It is only through this translation and cooperation between clinical and bench scientists that new insights into platelet biology and therapeutic development can occur and fulfill the potential of genetically informed medicine.
Acknowledgments
JDE and ADJ are funded by the National, Heart Lung and Blood Institute Division of Intramural Research Program. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. GL is funded by the Canada Research Chair program, the Canadian Institutes of Health Research (MOP #123382), and the Montreal Heart Institute Foundation.
Footnotes
Declaration of Interest
No authors of the manuscript have any competing interests to declare in relation to the contents of the manuscript.
Reference List
- 1.Chu SG, Becker RC, Berger PB et al. Mean platelet volume as a predictor of cardiovascular risk: a systematic review and meta-analysis. J.Thromb.Haemost 2010;8:148–156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Hennekens CH, Dyken ML, Fuster V. Aspirin as a therapeutic agent in cardiovascular disease: a statement for healthcare professionals from the American Heart Association. Circulation 1997;96:2751–2753. [DOI] [PubMed] [Google Scholar]
- 3.Sutcliffe P, Connock M, Gurung T et al. Aspirin for prophylactic use in the primary prevention of cardiovascular disease and cancer: a systematic review and overview of reviews. Health Technol.Assess 2013;17:1–253. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Karpatkin S Heterogeneity of human platelets. VI. Correlation of platelet function with platelet volume. Blood 1978;51:307–316. [PubMed] [Google Scholar]
- 5.Lance MD, Sloep M, Henskens YM, Marcus MA. Mean platelet volume as a diagnostic marker for cardiovascular disease: drawbacks of preanalytical conditions and measuring techniques. Clin.Appl.Thromb.Hemost 2012;18:561–568. [DOI] [PubMed] [Google Scholar]
- 6.Noris P, Melazzini F, Balduini CL. New roles for mean platelet volume measurement in the clinical practice? Platelets 2016;27:607–612. [DOI] [PubMed] [Google Scholar]
- 7.Puurunen M, Johnson AD. Mean platelet volume - A controversial marker of platelets that requires further unpacking. Thromb.Res 2017 [DOI] [PubMed]
- 8.Harrison P, Goodall AH. Studies on Mean Platelet Volume (MPV) - New Editorial Policy. Platelets 2016;27:605–606. [DOI] [PubMed] [Google Scholar]
- 9.Biino G, Santimone I, Minelli C et al. Age- and sex-related variations in platelet count in Italy: a proposal of reference ranges based on 40987 subjects’ data. PLoS.One 2013;8:e54289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Segal JB, Moliterno AR. Platelet counts differ by sex, ethnicity, and age in the United States. Ann.Epidemiol 2006;16:123–130. [DOI] [PubMed] [Google Scholar]
- 11.Sloan A, Gona P, Johnson AD. Cardiovascular correlates of platelet count and volume in the Framingham Heart Study. Ann.Epidemiol 2015;25:492–498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Evans DM, Frazer IH, Martin NG. Genetic and environmental causes of variation in basal levels of blood cells. Twin.Res 1999;2:250–257. [DOI] [PubMed] [Google Scholar]
- 13.Garner C, Tatu T, Reittie JE et al. Genetic influences on F cells and other hematologic variables: a twin heritability study. Blood 2000;95:342–346. [PubMed] [Google Scholar]
- 14.Johnson AD. The genetics of common variation affecting platelet development, function and pharmaceutical targeting. J.Thromb.Haemost 2011;9 Suppl 1:246–257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lemahieu V, Gastier JM, Francke U. Novel mutations in the Wiskott-Aldrich syndrome protein gene and their effects on transcriptional, translational, and clinical phenotypes. Hum.Mutat 1999;14:54–66. [DOI] [PubMed] [Google Scholar]
- 16.Maclachlan A, Watson SP, Morgan NV. Inherited platelet disorders: Insight from platelet genomics using next-generation sequencing. Platelets 20161–6. [DOI] [PMC free article] [PubMed]
- 17.Lentaigne C, Freson K, Laffan MA, Turro E, Ouwehand WH. Inherited platelet disorders: toward DNA-based diagnosis. Blood 2016;127:2814–2823. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Evans DM, Zhu G, Duffy DL et al. Multivariate QTL linkage analysis suggests a QTL for platelet count on chromosome 19q. Eur.J.Hum.Genet 2004;12:835–842. [DOI] [PubMed] [Google Scholar]
- 19.Eicher JD, Landowski C, Stackhouse B et al. GRASP v2.0: an update on the Genome-Wide Repository of Associations between SNPs and phenotypes. Nucleic Acids Res 2015;43:D799–D804. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Robinson MR, Wray NR, Visscher PM. Explaining additional genetic variation in complex traits. Trends Genet 2014;30:124–132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Meisinger C, Prokisch H, Gieger C et al. A genome-wide association study identifies three loci associated with mean platelet volume. Am.J.Hum.Genet 2009;84:66–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Soranzo N, Rendon A, Gieger C et al. A novel variant on chromosome 7q22.3 associated with mean platelet volume, counts, and function. Blood 2009;113:3831–3837. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Soranzo N, Spector TD, Mangino M et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat.Genet 2009;41:1182–1190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Gieger C, Radhakrishnan A, Cvejic A et al. New gene functions in megakaryopoiesis and platelet formation. Nature 2011;480:201–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Schick UM, Jain D, Hodonsky CJ et al. Genome-wide Association Study of Platelet Count Identifies Ancestry-Specific Loci in Hispanic/Latino Americans. Am.J.Hum.Genet 2016;98:229–242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kamatani Y, Matsuda K, Okada Y et al. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat.Genet 2010;42:210–215. [DOI] [PubMed] [Google Scholar]
- 27.Qayyum R, Snively BM, Ziv E et al. A meta-analysis and genome-wide association study of platelet count and mean platelet volume in african americans. PLoS.Genet 2012;8:e1002491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Li J, Glessner JT, Zhang H et al. GWAS of blood cell traits identifies novel associated loci and epistatic interactions in Caucasian and African-American children. Hum.Mol.Genet 2013;22:1457–1464. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Oh JH, Kim YK, Moon S, Kim YJ, Kim BJ. Genome-wide association study identifies candidate Loci associated with platelet count in koreans. Genomics Inform 2014;12:225–230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kim YK, Oh JH, Kim YJ et al. Influence of Genetic Variants in EGF and Other Genes on Hematological Traits in Korean Populations by a Genome-Wide Approach. Biomed.Res.Int 2015;2015:914965. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Shameer K, Denny JC, Ding K et al. A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum.Genet 2014;133:95–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Astle WJ, Elding H, Jiang T et al. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell 2016;167:1415–1429. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Kosmicki JA, Churchhouse CL, Rivas MA, Neale BM. Discovery of rare variants for complex phenotypes. Hum.Genet 2016;135:625–634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Auer PL, Johnsen JM, Johnson AD et al. Imputation of exome sequence variants into population- based samples and blood-cell-trait-associated loci in African Americans: NHLBI GO Exome Sequencing Project. Am.J.Hum.Genet 2012;91:794–808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Polfus LM, Khajuria RK, Schick UM et al. Whole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human Hematopoiesis. Am.J.Hum.Genet 2016;99:481–488. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Grove ML, Yu B, Cochran BJ et al. Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLoS.One 2013;8:e68095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Auer PL, Teumer A, Schick U et al. Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits. Nat.Genet 2014;46:629–634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Eicher JD, Chami N, Kacprowski T et al. Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals. Am.J.Hum.Genet 2016;99:40–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Iotchkova V, Huang J, Morris JA et al. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nat.Genet 2016;48:1303–1312. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Das S, Forer L, Schonherr S et al. Next-generation genotype imputation service and methods. Nat.Genet 2016;48:1284–1287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.McCarthy S, Das S, Kretzschmar W et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat.Genet 2016;48:1279–1283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Pickrell JK, Berisa T, Liu JZ et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat.Genet 2016;48:709–717. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Davey SG, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum.Mol.Genet 2014;23:R89–R98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Sansanayudh N, Anothaisintawee T, Muntham D et al. Mean platelet volume and coronary artery disease: a systematic review and meta-analysis. Int.J.Cardiol 2014;175:433–440. [DOI] [PubMed] [Google Scholar]
- 45.Sansanayudh N, Numthavaj P, Muntham D et al. Prognostic effect of mean platelet volume in patients with coronary artery disease. A systematic review and meta-analysis. Thromb.Haemost 2015;114:1299–1309. [DOI] [PubMed] [Google Scholar]
- 46.Beyan C, Beyan E. Mean platelet volume and cardiovascular risk factors. Eur.J.Intern.Med 2016;31:e15. [DOI] [PubMed] [Google Scholar]
- 47.Kircher M, Witten DM, Jain P et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat.Genet 2014;46:310–315. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Lecine P, Villeval JL, Vyas P et al. Mice lacking transcription factor NF-E2 provide in vivo validation of the proplatelet model of thrombocytopoiesis and show a platelet production defect that is intrinsic to megakaryocytes. Blood 1998;92:1608–1616. [PubMed] [Google Scholar]
- 49.Kauskot A, Vandenbriele C, Louwette S et al. PEAR1 attenuates megakaryopoiesis via control of the PI3K/PTEN pathway. Blood 2013;121:5208–5217. [DOI] [PubMed] [Google Scholar]
- 50.Izzi B, Pistoni M, Cludts K et al. Allele-specific DNA methylation reinforces PEAR1 enhancer activity. Blood 2016;128:1003–1012. [DOI] [PubMed] [Google Scholar]
- 51.Paul DS, Nisbet JP, Yang TP et al. Maps of open chromatin guide the functional follow-up of genome-wide association signals: application to hematological traits. PLoS.Genet 2011;7:e1002139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Kitajima K, Kojima M, Kondo S, Takeuchi T. A role of jumonji gene in proliferation but not differentiation of megakaryocyte lineage cells. Exp.Hematol 2001;29:507–514. [DOI] [PubMed] [Google Scholar]
- 53.Sankaran VG, Orkin SH. Genome-wide association studies of hematologic phenotypes: a window into human hematopoiesis. Curr.Opin.Genet.Dev 2013;23:339–344. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Johnson AD, Yanek LR, Chen MH et al. Genome-wide meta-analyses identifies seven loci associated with platelet aggregation in response to agonists. Nat.Genet 2010;42:608–613. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Eicher JD, Xue L, Ben-Shlomo Y, Beswick A, and Johnson AD Replication of platelet reactivity genome-wide association single nucleotide polymorphisms in the Caerphilly Prospective Study. International Society for Thrombosis and Haemostasis Research (submitted abstract) 2015.
- 56.Nurnberg ST, Rendon A, Smethurst PA et al. A GWAS sequence variant for platelet volume marks an alternative DNM3 promoter in megakaryocytes near a MEIS1 binding site. Blood 2012;120:4859–4868. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Simon LM, Chen ES, Edelstein LC et al. Integrative Multi-omic Analysis of Human Platelet eQTLs Reveals Alternative Start Site in Mitofusin 2. Am.J.Hum.Genet 2016;98:883–897. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Begonja AJ, Pluthero FG, Suphamungmee W et al. FlnA binding to PACSIN2 F-BAR domain regulates membrane tubulation in megakaryocytes and platelets. Blood 2015;126:80–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Kunishima S, Okuno Y, Yoshida K et al. ACTN1 mutations cause congenital macrothrombocytopenia. Am.J.Hum.Genet 2013;92:431–438. [DOI] [PMC free article] [PubMed] [Google Scholar]