Original Article | Published:

Genome-wide linkage and association analysis of cardiometabolic phenotypes in Hispanic Americans

Journal of Human Genetics volume 62, pages 175184 (2017) | Download Citation

Abstract

Linkage studies of complex genetic diseases have been largely replaced by genome-wide association studies, due in part to limited success in complex trait discovery. However, recent interest in rare and low-frequency variants motivates re-examination of family-based methods. In this study, we investigated the performance of two-point linkage analysis for over 1.6 million single-nucleotide polymorphisms (SNPs) combined with single variant association analysis to identify high impact variants, which are both strongly linked and associated with cardiometabolic traits in up to 1414 Hispanics from the Insulin Resistance Atherosclerosis Family Study (IRASFS). Evaluation of all 50 phenotypes yielded 83 557 000 LOD (logarithm of the odds) scores, with 9214 LOD scores 3.0, 845 4.0 and 89 5.0, with a maximal LOD score of 6.49 (rs12956744 in the LAMA1 gene for tumor necrosis factor-α (TNFα) receptor 2). Twenty-seven variants were associated with P<0.005 as well as having an LOD score >4, including variants in the NFIB gene under a linkage peak with TNFα receptor 2 levels on chromosome 9. Linkage regions of interest included a broad peak (31 Mb) on chromosome 1q with acute insulin response (max LOD=5.37). This region was previously documented with type 2 diabetes in family-based studies, providing support for the validity of these results. Overall, we have demonstrated the utility of two-point linkage and association in comprehensive genome-wide array-based SNP genotypes.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , & Family-based designs for genome-wide association studies. Nat. Rev. Genet. 12, 465–474 (2011).

  2. 2.

    & Relatedness in the post-genomic era: is it still useful? Nat. Rev. Genet. 16, 33–44 (2015).

  3. 3.

    , , , , , et al. Molecular basis of a linkage peak: exome sequencing and family-based analysis identify a rare genetic variant in the ADIPOQ gene in the IRAS Family Study. Hum. Mol. Genet. 19, 4112–4120 (2010).

  4. 4.

    Will family studies return to prominence in human genetics and genomics? Rare variants and linkage analysis of complex traits. Genes Genomics 33, 1–8 (2011).

  5. 5.

    , & A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing. Front. Genet. 6, 149 (2015).

  6. 6.

    & Linkage analysis and the study of Mendelian disease in the era of whole exome and genome sequencing. Brief. Funct. Genomics 13, 378–383 (2014).

  7. 7.

    , & Genetic linkage analysis in the age of whole-genome sequencing. Nat. Rev. Genet. 16, 275–284 (2015).

  8. 8.

    & Power of family-based association designs to detect rare variants in large pedigrees using imputed genotypes. Genet. Epidemiol. 38, 1–9 (2014).

  9. 9.

    , , , , , et al. Genome-wide family-based linkage analysis of exome chip variants and cardiometabolic risk. Genet. Epidemiol. 38, 345–352 (2014).

  10. 10.

    , , , , , et al. Empirical characteristics of family-based linkage to a complex trait: the ADIPOQ region and adiponectin levels. Hum. Genet. 134, 203–213 (2015).

  11. 11.

    , , , , , et al. Genome-wide association analysis confirms and extends the association of SLC2A9 with serum uric acid levels to Mexican Americans. Front. Genet. 4, 279 (2013).

  12. 12.

    , & Integration of linkage analyses and disease association studies. Genet. Epidemiol. 12, 653–658 (1995).

  13. 13.

    , , , , , et al. A meta-analytic investigation of linkage and association of common leptin receptor (LEPR) polymorphisms with body mass index and waist circumference. Int. J. Obesity Relat. Metab. Disord. 26, 640–646 (2002).

  14. 14.

    , , , , , et al. Recessive mutations in a distal PTF1A enhancer cause isolated pancreatic agenesis. Nat. Genet. 46, 61–64 (2014).

  15. 15.

    , , , , , et al. Genetic epidemiology of insulin resistance and visceral adiposity. The IRAS Family Study design and methods. Ann. Epidemiol. 13, 211–217 (2003).

  16. 16.

    , , , , , et al. Genetic variants associated with quantitative glucose homeostasis traits translate to type 2 diabetes in Mexican Americans: the GUARDIAN (Genetics Underlying Diabetes in Hispanics) Consortium. Diabetes 64, 1853–1866 (2014).

  17. 17.

    & PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am. J. Hum. Genet 63, 259–266 (1998).

  18. 18.

    , & A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2012).

  19. 19.

    , & A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5, e1000529 (2009).

  20. 20.

    & Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198–1211 (1998).

  21. 21.

    , & Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19, 1655–1664 (2009).

  22. 22.

    , , & Linkage and association mapping of a chromosome 1q21–q24 type 2 diabetes susceptibility locus in northern European Caucasians. Diabetes 53, 492–499 (2004).

  23. 23.

    , , , , , et al. Linkage of the metabolic syndrome to 1q23–q31 in Hispanic families: the Insulin Resistance Atherosclerosis Study Family Study. Diabetes 53, 1170–1174 (2004).

  24. 24.

    , , , , , et al. A genomewide scan for loci predisposing to type 2 diabetes in a UK population (the Diabetes UK Warren 2 Repository): analysis of 573 pedigrees provides independent replication of a susceptibility locus on chromosome 1q. Am. J. Hum. Genet. 69, 553–569 (2001).

  25. 25.

    , , , , , et al. Genomewide search for type 2 diabetes-susceptibility genes in French whites: evidence for a novel susceptibility locus for early-onset diabetes on chromosome 3q27-qter and independent replication of a type 2-diabetes locus on chromosome 1q21–q24. Am. J. Hum. Genet 67, 1470–1480 (2000).

  26. 26.

    , , , , & A chemotactic peptide from laminin alpha 5 functions as a regulator of inflammatory immune responses via TNF alpha-mediated signaling. J. Immunol. (Baltimore, Md: 1950) 174, 1621–1629 (2005).

  27. 27.

    , , , , , et al. Borderline personality disorder and childhood maltreatment: a genome-wide methylation analysis. Genes Brain Behav. 14, 177–188 (2015).

  28. 28.

    , , , , , et al. Neuroblastoma-derived secretory protein messenger RNA levels correlate with high-risk neuroblastoma. J. Pediatr. Surg. 42, 148–152 (2007).

  29. 29.

    , , , , , et al. Neuroblastoma-derived secretory protein is a novel secreted factor overexpressed in neuroblastoma. Mol. Cancer Ther. 8, 2478–2489 (2009).

  30. 30.

    , & Loss of the dystonia-associated protein torsinA selectively disrupts the neuronal nuclear envelope. Neuron 48, 923–932 (2005).

  31. 31.

    , , , & LULL1 retargets TorsinA to the nuclear envelope revealing an activity that is impaired by the DYT1 dystonia mutation. Mol. Biol. Cell 20, 2661–2672 (2009).

  32. 32.

    , , , , , et al. Mutation in TOR1AIP1 encoding LAP1B in a form of muscular dystrophy: a novel gene related to nuclear envelopathies. Neuromusc. Disord. 24, 624–633 (2014).

  33. 33.

    , , , , , et al. Obesity susceptibility genetic variants identified from recent genome-wide association studies: implications in a Chinese population. J. Clin. Endocrinol. Metab. 95, 1395–1403 (2010).

  34. 34.

    , , , , , et al. Contribution of common genetic variants to obesity and obesity-related traits in Mexican children and adults. PLoS ONE 8, e70640 (2013).

  35. 35.

    , , , , , et al. Genome-wide study of hypomethylated and induced genes in patients with liver cancer unravels novel anticancer targets. Clin. Cancer Res. 20, 3118–3132 (2014).

  36. 36.

    , , , , , et al. Identification of thyroid carcinoma related genes with mRMR and shortest path approaches. PLoS ONE 9, e94022 (2014).

  37. 37.

    , , , , , et al. RASAL2 activates RAC1 to promote triple-negative breast cancer progression. J. Clin. Invest. 124, 5291–5304 (2014).

  38. 38.

    , , , , , et al. RASAL2 down-regulation in ovarian cancer promotes epithelial-mesenchymal transition and metastasis. Oncotarget 5, 6734–6745 (2014).

  39. 39.

    & RASAL2 promotes lung cancer metastasis through epithelial-mesenchymal transition. Biochem. Biophys. Res. Commun. 455, 358–362 (2014).

  40. 40.

    , , , & Gpr1 is an active chemerin receptor influencing glucose homeostasis in obese mice. J. Endocrinol. 222, 201–215 (2014).

  41. 41.

    , , , , , et al. Disruption of the chemokine-like receptor-1 (CMKLR1) gene is associated with reduced adiposity and glucose intolerance. Endocrinology 153, 672–682 (2012).

  42. 42.

    , , , , , et al. Chemerin is a novel adipocyte-derived factor inducing insulin resistance in primary human skeletal muscle cells. Diabetes 58, 2731–2740 (2009).

  43. 43.

    , , , , , et al. Chemokine-like receptor 1 deficiency does not affect the development of insulin resistance and nonalcoholic fatty liver disease in mice. PLoS ONE 9, e96345 (2014).

  44. 44.

    , , , , , et al. Effect of lifestyle modification on serum chemerin concentration and its association with insulin sensitivity in overweight and obese adults with type 2 diabetes. Clin Endocrinol (Oxf) 80, 825–833 (2014).

  45. 45.

    , & Chemerin: a potential endocrine link between obesity and type 2 diabetes. Endocrine 42, 243–251 (2012).

  46. 46.

    , , , , , et al. Identification of candidate susceptibility genes for colorectal cancer through eQTL analysis. Carcinogenesis 35, 2039–2046 (2014).

  47. 47.

    , , , , , et al. Common variants in the Na-coupled bicarbonate transporter genes and salt sensitivity of blood pressure: the GenSalt study. J. Hum. Hypertens. 30, 543–548 (2015).

  48. 48.

    , , & Cation-coupled bicarbonate transporters. Compr. Physiol. 4, 1605–1637 (2014).

  49. 49.

    , , , , , et al. Keratins 8 and 18 are type II acute-phase responsive genes overexpressed in human liver disease. Liver Int. 35, 1203–1212 (2015).

  50. 50.

    , , , , , et al. International Union of Basic and Clinical Pharmacology. XCIV. Adhesion G protein-coupled receptors. Pharmacol. Rev. 67, 338–367 (2015).

  51. 51.

    , & From the black widow spider to human behavior: Latrophilins, a relatively unknown class of G protein-coupled receptors, are implicated in psychiatric disorders. Am. J. Med. Genet. B 156b, 1–10 (2011).

  52. 52.

    , , , , , et al. Structural basis of latrophilin-FLRT interaction. Structure (London, England: 1993) 23, 774–781 (2015).

  53. 53.

    , , , , , et al. FLRT proteins are endogenous latrophilin ligands and regulate excitatory synapse development. Neuron 73, 903–910 (2012).

  54. 54.

    , , , , , et al. Influence of a latrophilin 3 (LPHN3) risk haplotype on event-related potential measures of cognitive response control in attention-deficit hyperactivity disorder (ADHD). Eur. Neuropsychopharmacol. 23, 458–468 (2013).

  55. 55.

    , , , , , et al. Contribution of LPHN3 to the genetic susceptibility to ADHD in adulthood: a replication study. Genes Brain Behav. 10, 149–157 (2011).

  56. 56.

    , , , , , et al. A common variant of the latrophilin 3 gene, LPHN3, confers susceptibility to ADHD and predicts effectiveness of stimulant medication. Mol. Psychiatry 15, 1053–1066 (2010).

  57. 57.

    , , , , , et al. LPHN3 and attention-deficit/hyperactivity disorder: a susceptibility and pharmacogenetic study. Genes Brain Behav 14, 419–427 (2015).

Download references

Acknowledgements

This work was supported by the Grants R01 HG007112 (to DWB and CDL) and R01 DK087914 (to MCYN). The GUARDIAN study, which contributed the IRASFS GWAS genotypes to this project is supported by Grant R01 DK085175 (to LEW), and the IRASFS study was supported by HL060944, HL061019 and HL060919. The provision of GWAS genotyping data was supported, in part, by UL1TR000124 (CTSI), and DK063491 (DRC). The provision of exome chip data was supported, in part, by the Department of Internal Medicine at University of Michigan, the Doris Duke Medical Foundation and R01 DK106621 (to EKS). Computational support was provided, in part, by the Center for Public Health Genomics at Wake Forest School of Medicine.

Author information

Affiliations

  1. Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Jacklyn N Hellwege
    • , Nicholette D Palmer
    • , Latchezar Dimitrov
    • , Jacob M Keaton
    • , Keri L Tabb
    • , Maggie C Y Ng
    • , Gregory A Hawkins
    •  & Donald W Bowden
  2. Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Jacklyn N Hellwege
    • , Nicholette D Palmer
    • , Jacob M Keaton
    • , Keri L Tabb
    • , Maggie C Y Ng
    •  & Donald W Bowden
  3. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Nicholette D Palmer
    • , Keri L Tabb
    •  & Donald W Bowden
  4. Center for Public Health Genomics, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Nicholette D Palmer
    • , Satria Sajuthi
    • , Maggie C Y Ng
    • , Gregory A Hawkins
    • , Carl D Langefeld
    •  & Lynne E Wagenknecht
  5. Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Nicholette D Palmer
    • , Jacob M Keaton
    • , Satria Sajuthi
    •  & Carl D Langefeld
  6. Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Satria Sajuthi
    •  & Carl D Langefeld
  7. Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA

    • Kent D Taylor
    • , Yii-Der Ida Chen
    •  & Jerome I Rotter
  8. Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, MI, USA

    • Elizabeth K Speliotes
  9. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    • Elizabeth K Speliotes
  10. Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    • Jirong Long
  11. Department of Medicine, University of Texas Health Science Center, San Antonio, TX, USA

    • Carlos Lorenzo
  12. Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, CO, USA

    • Jill M Norris
  13. Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA

    • Lynne E Wagenknecht

Authors

  1. Search for Jacklyn N Hellwege in:

  2. Search for Nicholette D Palmer in:

  3. Search for Latchezar Dimitrov in:

  4. Search for Jacob M Keaton in:

  5. Search for Keri L Tabb in:

  6. Search for Satria Sajuthi in:

  7. Search for Kent D Taylor in:

  8. Search for Maggie C Y Ng in:

  9. Search for Elizabeth K Speliotes in:

  10. Search for Gregory A Hawkins in:

  11. Search for Jirong Long in:

  12. Search for Yii-Der Ida Chen in:

  13. Search for Carlos Lorenzo in:

  14. Search for Jill M Norris in:

  15. Search for Jerome I Rotter in:

  16. Search for Carl D Langefeld in:

  17. Search for Lynne E Wagenknecht in:

  18. Search for Donald W Bowden in:

Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to Donald W Bowden.

Supplementary information

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/jhg.2016.103

Supplementary Information accompanies the paper on Journal of Human Genetics website (http://www.nature.com/jhg)

Further reading