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A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer’s disease

European Journal of Human Genetics volume 28, pages 1734–1742 (2020)Cite this article

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Abstract

To analyze pedigrees with quantitative trait (QT) and sequence data, we developed a rare variant (RV) quantitative nonparametric linkage (QNPL) method, which evaluates sharing of minor alleles. RV-QNPL has greater power than the traditional QNPL that tests for excess sharing of minor and major alleles. RV-QNPL is robust to population substructure and admixture, locus heterogeneity, and inclusion of nonpathogenic variants and can be readily applied outside of coding regions. When QNPL was used to analyze common variants, it often led to loci mapping to large intervals, e.g., >40 Mb. In contrast, when RVs are analyzed, regions are well defined, e.g., a gene. Using simulation studies, we demonstrate that RV-QNPL is substantially more powerful than applying traditional QNPL methods to analyze RVs. RV-QNPL was also applied to analyze age-at-onset (AAO) data for 107 late-onset Alzheimer’s disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with whole-genome sequence data. When AAO of AD was analyzed regardless of APOE ε4 status, suggestive linkage (LOD = 2.4) was observed with RVs in KNDC1 and nominally significant linkage (p < 0.05) was observed with RVs in LOAD genes ABCA7 and IQCK. When AAO of AD was analyzed for APOE ε4 positive family members, nominally significant linkage was observed with RVs in APOE, while when AAO of AD was analyzed for APOE ε4 negative family members, nominal significance was observed for IQCK and ADAMTS1. RV-QNPL provides a powerful resource to analyze QTs in families to elucidate their genetic etiology.

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Fig. 1: Pedigree structures used in simulation studies.
Fig. 2: Exome-wide power for RV-QNPL and CHP-QNPL in extended pedigrees.

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Acknowledgements

We wish to thank the family members who participated in the Alzheimer Disease Sequencing Project and made this research possible. We also thank Katherine Montague for proof-reading this manuscript. The datasets used for the analyses in this manuscript were obtained from the database of Genotypes and Phenotypes (dbGaP) through dbGaP accession study number phs000572.v7.p4. We would like to thank dbGaP for distributing the data used in this study. We thank contributors, including the Alzheimer’s disease Centers who collected samples used in the NIA-LOAD study, as well as patients and their families, whose help and participation made this work possible. We also acknowledge the Genetic Studies of Alzheimer’s disease in Caribbean Hispanics (EFIGA) study participants and the EFIGA research and support staff for their contributions to this study. Complete acknowledgments can be found in the Supplemental Acknowledgments.

Funding

The National Institute on Aging (NIA)-LOAD supported the collection of samples used in this study through NIA grants U24AG026395, R01AG041797, 5R37AG015473, RF1AG015473, and R56AG051876. This work was also supported by grants from the National Human Genome Research Institute R01 HG008972 and NIA RF1 AG058131.

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Authors and Affiliations

  1. Center for Statistical Genetics, Baylor College of Medicine, Houston, TX, 77030, USA

    Linhai Zhao, Zhihui Zhang & Suzanne M. Leal

  2. Center for Statistical Genetics, Columbia University, New York, NY, 10027, USA

    Zhihui Zhang, Gao T. Wang & Suzanne M. Leal

  3. Department of Neurology, Taub Institute on Alzheimer’s Disease and the Aging Brain, and Gertrude H. Sergievsky Center, Columbia University, New York, NY, 10027, USA

    Zhihui Zhang, Sandra M. Barral Rodriguez, Badri N. Vardarajan, Richard Mayeux, Gao T. Wang & Suzanne M. Leal

  4. Department of Neuroscience and Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Alan E. Renton & Alison M. Goate

  5. Department of Neuroscience and Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, 10029, USA

    Alison M. Goate

  6. Department of Human Genetics, The University of Chicago, Chicago, IL, 60637, USA

    Gao T. Wang

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  1. Linhai Zhao
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Correspondence to Suzanne M. Leal.

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Zhao, L., Zhang, Z., Rodriguez, S.M.B. et al. A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer’s disease. Eur J Hum Genet 28, 1734–1742 (2020). https://doi.org/10.1038/s41431-020-0703-z

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