There are several sources of information available about the etiology of common complex traits (of which our primary interest is in psychiatric disorders). We have epidemiological, genetic, and evolutionary information to consider in thinking about how genetic and environmental factors interact to influence phenotypes. Epidemiological data tells us something about the prevalence (and its variation within and between populations), its correlation among relatives, and relationships to specific environmental factors. Evolutionary data informs about the rates of mutation, the effects of demographic structure (including assortative mating, migration, drift, inbreeding and the like), and potential effects of natural selection in molding the genetic portion of the etiological architecture of a trait. Genetic data tells us of evidence of linkage (i.e. involvement - or lack thereof - of genes located in certain chromosomal regions), or direct genotypic associations (i.e. etiological effects - or lack thereof - of measured genes, or genes in LD with them) of specific loci. Rarely are these data looked at jointly, however. In this application, we propose to further develop and apply our methods for simultaneously considering all of these data types in an effort to better understand the true etiological architecture. We will work with various simulation-based approaches to consider what phenogenetic models are compatible with the existing data we have about evolution, epidemiology of a given trait, and past attempts at gene finding for those traits (both successful, and, in what we believe is a novel twist, unsuccessful ones as well). Each of these data types informs about what range of etiological models would be plausible for a given disease, although of course there is no way to infer actual truth. Our goal is to eliminate from consideration models, which are inconsistent with existing knowledge, and to compare the power of various study designs and inferential methods under the range of plausible set of etiological models. Models which would have predicted that previous studies should have found the genes would be rejected, as would models inconsistent with our existing epidemiological and evolutionary information. We will explore the set of plausible models and further develop our set of inferential analysis methods for joint linkage and LD analysis in the sort of heterogeneous data structures that we expect will be the most powerful for prospective gene identification in this highly complex psychiatric traits, for which we have massive amounts of data, but unfortunately little real knowledge extracted from them.

Public Health Relevance

The overall aim of this study is to develop and apply new methods to help us better understand elements of the etiology of common diseases such as schizophrenia, bipolar, and Alzheimer disease. These methods, based both on evolutionary and epidemiological inference should allow us to more efficiently exploit the accomplishments of the recent biotechnological revolution for unraveling the complex etiological architecture of these diseases, both through more powerful statistical analysis and optimization of experimental design. It is hoped that better understanding of the true nature of these disorders will be useful for development of prospective public health strategies.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Lehner, Thomas
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Columbia University (N.Y.)
Schools of Medicine
New York
United States
Zip Code
Yan, W L; Li, X S; Wang, Q et al. (2015) Overweight, high blood pressure and impaired fasting glucose in Uyghur, Han, and Kazakh Chinese children and adolescents. Ethn Health 20:365-75
Weiss, Kenneth M; Lambert, Brian W (2014) What type of person are you? Old-fashioned thinking even in modern science. Cold Spring Harb Perspect Biol 6:
Janicki, S C; Park, N; Cheng, R et al. (2014) Estrogen receptor ? variants affect age at onset of Alzheimer's disease in a multiethnic female cohort. Dement Geriatr Cogn Disord 38:200-13
Gertz, Edward Michael; Hiekkalinna, Tero; Digabel, Sébastien Le et al. (2014) PSEUDOMARKER 2.0: efficient computation of likelihoods using NOMAD. BMC Bioinformatics 15:47
Janicki, S C; Park, N; Cheng, R et al. (2013) Aromatase variants modify risk for Alzheimer's disease in a multiethnic female cohort. Dement Geriatr Cogn Disord 35:340-6
Rhinn, Herve; Fujita, Ryousuke; Qiang, Liang et al. (2013) Integrative genomics identifies APOE ?4 effectors in Alzheimer's disease. Nature 500:45-50
Hiekkalinna, Tero; Goring, Harald H H; Terwilliger, Joseph D (2012) On the validity of the likelihood ratio test and consistency of resulting parameter estimates in joint linkage and linkage disequilibrium analysis under improperly specified parametric models. Ann Hum Genet 76:63-73
Hiekkalinna, Tero; Goring, Harald H H; Lambert, Brian et al. (2012) On the statistical properties of family-based association tests in datasets containing both pedigrees and unrelated case-control samples. Eur J Hum Genet 20:217-23
Hiekkalinna, Tero; Schäffer, Alejandro A; Lambert, Brian et al. (2011) PSEUDOMARKER: a powerful program for joint linkage and/or linkage disequilibrium analysis on mixtures of singletons and related individuals. Hum Hered 71:256-66
Weiss, Kenneth M; Lambert, Brian W (2011) When the time seems ripe: eugenics, the annals, and the subtle persistence of typological thinking. Ann Hum Genet 75:334-43

Showing the most recent 10 out of 12 publications