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Author: Hunter, Michael D.

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1.	Bard, David E. Hunter, Michael D. Beasley, William H. Rodgers, Joseph Lee Meredith, Kelly M.
1.	Biometric Nonlinear Growth Curves for Cognitive Development among NLSY Children and Youth Presented: Marseille, France, Behavior Genetics Association (BGA) Annual Meeting, June-July 2013 Cohort(s): Children of the NLSY79, NLSY79 Publisher: Behavior Genetics Association Keyword(s): Cognitive Ability; Cognitive Development; Digit Span (also see Memory for Digit Span - WISC); Kinship; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Peabody Picture Vocabulary Test (PPVT); Siblings Permission to reprint the abstract has not been received from the publisher. Recent advances in building and fitting growth curve and multi-level models that are biometrically informed (McArdle, 2006; McArdle & Plassman, 2009; McArdle & Prescott, 2005; McGue & Christensen, 2002; Reynolds, Finkel, Gatz, & Pedersen, 2002) were used to study cognitive development and decline (or slowed growth) in the National Longitudinal Survey of Youth- Child/Young Adult (NLSYC/YA) dataset. Among the highest quality outcome data in the NLSY files are indicators of cognitive ability, collected longitudinally. These data includes PIAT-Math, PIAT-Reading Recognition and PIAT-Reading Comprehension scores in a complete longitudinal stream (up to attrition) from ages 5 to 14, as well as PPVT verbal abilities, Digit Span scores, and cognitive developmental milestone indicators during toddler and preschool years. Building off of longitudinal methodologies outside of behavior genetics (Grimm, Ram, & Hamagami, 2011; McArdle, Ferrer-Caja, Hamagami, & Woodcock, 2002; Pinheiro & Bates, 2000), this empirical application will also contribute to biometric analytic developments utilizing "fully" nonlinear (e.g., exponential; Davidian & Giltinan, 1995) growth models that better capture developmental and aging-related changes in cognition. Multivariate models were also examined to explore cognitive mediational hypotheses of whether early cognitive milestones could predict later developmental trajectories of PIAT, PPVT, and Digit Span growth. These models predicted both variation in level effects (early age ability level) and growth/decline effects over time (developmental changes in cognition). Motivation for these analyses closely coincide with the convergence of evidence surrounding critical periods of development between the ages of 0 and 5 (Shonkoff & Phillips, 2000). Again, interest will move beyond simple associations of early cognition and childhood cognitive development to questions of whether individual differences in genetic or environmental sources of variance best explain these associations via multivariate biometric mediation modeling.
	Bibliography Citation Bard, David E., Michael D. Hunter, William H. Beasley, Joseph Lee Rodgers and Kelly M. Meredith. "Biometric Nonlinear Growth Curves for Cognitive Development among NLSY Children and Youth." Presented: Marseille, France, Behavior Genetics Association (BGA) Annual Meeting, June-July 2013.

2.	Beasley, William H. Bard, David E. Hunter, Michael D. Meredith, Kelly M. Rodgers, Joseph Lee
2.	NLSY Kinship Links: Creating Biometrical Design Structures from Cross-Generational Data Presented: Marseille, France, Behavior Genetics Association (BGA) Annual Meeting, June-July 2013 Cohort(s): Children of the NLSY79, NLSY79, NLSY97 Publisher: Behavior Genetics Association Keyword(s): Cognitive Ability; Cognitive Development; Digit Span (also see Memory for Digit Span - WISC); Genetics; Kinship; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Peabody Picture Vocabulary Test (PPVT); Siblings Permission to reprint the abstract has not been received from the publisher. In this paper, we present innovative NLSY designs. We begin with a review of the Mother-Daughter-Aunt-Niece (MDAN) design (Rodgers et al. 2008) and expand this to include other relationships simultaneously, including the 5,000 NLSYC first cousins. Following we discuss the potential for limited three-generational designs using the available information about the parents of the original NLSY79 respondents. Finally, we discuss how incorporating a third dataset, (the NLSY97) provides a ‘"phantom mother’" design, developed by (age, SES, family, etc.) matching of the NLSYC to the NLSY97 respondents, and assigning NLSY79 mothers to NLSY97 respondents across these matches.
	Bibliography Citation Beasley, William H., David E. Bard, Michael D. Hunter, Kelly M. Meredith and Joseph Lee Rodgers. "NLSY Kinship Links: Creating Biometrical Design Structures from Cross-Generational Data." Presented: Marseille, France, Behavior Genetics Association (BGA) Annual Meeting, June-July 2013.

3.	Hunter, Michael D.
3.	State Space Dynamic Mixture Modeling: Finding People with Similar Patterns of Change Ph.D. Dissertation, Department of Psychology, University of Oklahoma, 2014 Cohort(s): Children of the NLSY79 Publisher: Department of Psychology, University of Oklahoma Keyword(s): Academic Development; Digit Span (also see Memory for Digit Span - WISC); Genetics; Kinship; Modeling, Multilevel; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Peabody Picture Vocabulary Test (PPVT); Siblings Permission to reprint the abstract has not been received from the publisher. Increasingly, psychologists encounter data in which several individuals have been measured on multiple variables over numerous occasions. Many of the current methods for this situation combine the data, assuming everyone is a randomly equivalent to everyone else. The extreme alternative on the other side is to separately analyze each person's data, assuming no one is similar to anyone else. This dissertation proposes a method as a compromise between these two extremes. The goal of the method is to find people in the data that are undergoing similar change processes over time. Data were simulated under various conditions to explore what factors influenced the ability of the method to correctly estimate the change process and accurately find people with the same process. It was found that sample size had the greatest positive influence on parameter estimation and the dimension of the change process had the greatest positive impact on correctly grouping people together, likely due to the distinctiveness of their patterns of change. With some success in simulation, the method was applied to an archival data source reflecting cognitive growth in the National Longitudinal Survey of Youth Children data. This analysis suggested that the genetic effects operating between people on their cognitive development may be quite different from their within-person effects, but also revealed a limitation for the method on large sample sizes. Once software improvements are made to the method, its applicability to large, real data should be reevaluated. State space mixture modeling, in its current form, offers one of the best-performing methods for simultaneously drawing conclusions about individual change processes while also analyzing multiple people.
	Bibliography Citation Hunter, Michael D. State Space Dynamic Mixture Modeling: Finding People with Similar Patterns of Change. Ph.D. Dissertation, Department of Psychology, University of Oklahoma, 2014.

4.	Hunter, Michael D. Bard, David E. Beasley, William H. Meredith, Kelly M. Rodgers, Joseph Lee
4.	A Dynamic Mixture Biometric Model of Cognitive Development in the NLSY Children Presented: Charlottesville VA, Behavior Genetics Association Annual Meeting, June 2014 Cohort(s): Children of the NLSY79 Publisher: Behavior Genetics Association Keyword(s): Digit Span (also see Memory for Digit Span - WISC); Genetics; Kinship; Modeling, Multilevel; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Peabody Picture Vocabulary Test (PPVT); Siblings Permission to reprint the abstract has not been received from the publisher. A novel method of combining within-person and between-person variability in a biometrically informed model was used to examine nonlinear cognitive development in the National Longitudinal Survey of Youth-Child/Young Adult (NLSYC/YA) dataset. Entirely within person biometric models (e.g. Molenaar 2010) can be fit, but generally assume that all persons are heterogeneous. By contrast, conventional between-person biometric models (e.g. Martin & Eaves 1977) make the opposite assumption: that the sample is uniformly homogeneous. State space mixture modeling (SSMMing) is a middle ground. SSMMs make a within-person longitudinal biometric model for each pair of genetically related participants to account for the idiographic nature of genetic and developmental variability (Nesselroade, Gerstorf, Hardy, and Ram 2007; Molenaar, Boomsma, and Dolan 1993). Simultaneously, SSMMs allow for a finite number of groups that are within-group homogeneous and between-group heterogeneous to allow for uniformity in development among some people. The longitudinal model in SSMMs has both autoregressive and linear slope components with individually estimated growth trajectories. Hence, nonlinear patterns of change are allowed in the context of linear modeling. Five longitudinally measured cognitive variables (PIAT Reading Recognition, Reading Comprehension, and Math; PPVT; and Digit Span) from the NLSYC are used both to illustrate SSMMs as a method and to provide insight into this important process. The finding that cognitive ability is highly heritable between individuals was replicated in cross-sectional subsets of the NLSYC. However, the within-person longitudinal model showed minimal contribution from additive genetic variance across the five cognitive variables. A SSMM with two groups found a small subgroup in which cognitive ability was heritable within persons, but for the majority of individuals studied the intraindividual variance was dominated by common and specific environmental factors. The structure of intraindividual heritability of cognitive ability thus appears quite different from that found in conventional between person biometric modeling.
	Bibliography Citation Hunter, Michael D., David E. Bard, William H. Beasley, Kelly M. Meredith and Joseph Lee Rodgers. "A Dynamic Mixture Biometric Model of Cognitive Development in the NLSY Children." Presented: Charlottesville VA, Behavior Genetics Association Annual Meeting, June 2014.

5.	Hunter, Michael D. Garrison, S. Mason Burt, S. Alexandra Rodgers, Joseph Lee
5.	The Analytic Identification of Variance Component Models Common to Behavior Genetics Behavior Genetics 51 (July 2021): 425-437. Also: https://link.springer.com/article/10.1007%2Fs10519-021-10055-x Cohort(s): Children of the NLSY79, NLSY79 Publisher: Behavior Genetics Association Keyword(s): Family Environment; Intergenerational Patterns/Transmission; Modeling; Research Methodology; Siblings Permission to reprint the abstract has not been received from the publisher. Many behavior genetics models follow the same general structure. We describe this general structure and analytically derive simple criteria for its identification. In particular, we find that variance components can be uniquely estimated whenever the relatedness matrices that define the components are linearly independent (i.e., not confounded). Thus, we emphasize determining which variance components can be identified given a set of genetic and environmental relationships, rather than the estimation procedures. We validate the identification criteria with several well-known models, and further apply them to several less common models. The first model distinguishes child-rearing environment from extended family environment. The second model adds a gene-by-common-environment interaction term in sets of twins reared apart and together. The third model separates measured-genomic relatedness from the scanner site variation in a hypothetical functional magnetic resonance imaging study. The computationally easy analytic identification criteria allow researchers to quickly address model identification issues and define novel variance components, facilitating the development of new research questions.
	Bibliography Citation Hunter, Michael D., S. Mason Garrison, S. Alexandra Burt and Joseph Lee Rodgers. "The Analytic Identification of Variance Component Models Common to Behavior Genetics." Behavior Genetics 51 (July 2021): 425-437.

6.	Rodgers, Joseph Lee Beasley, William H. Bard, David E. Meredith, Kelly M. Hunter, Michael D. Johnson, Amber Buster, Maury Allen Li, Chengchang
6.	The NLSY Kinship Links: Using the NLSY79 and NLSY-Children Data to Conduct Genetically-Informed and Family-Oriented Research Behavior Genetics 46,4 (July 2016): 538-551. Also: http://link.springer.com/article/10.1007/s10519-016-9785-3 Cohort(s): Children of the NLSY79, NLSY79 Publisher: Behavior Genetics Association Keyword(s): Body Mass Index (BMI); Data Quality/Consistency; Genetics; Height; Intergenerational Patterns/Transmission; Kinship; Modeling, Multilevel; Siblings; Weight Permission to reprint the abstract has not been received from the publisher. The National Longitudinal Survey of Youth datasets (NLSY79; NLSY-Children/Young Adults; NLSY97) have extensive family pedigree information contained within them. These data sources are based on probability sampling, a longitudinal design, and a cross-generational and within-family data structure, with hundreds of phenotypes relevant to behavior genetic (BG) researchers, as well as to other developmental and family researchers. These datasets provide a unique and powerful source of information for BG researchers. But much of the information required for biometrical modeling has been hidden, and has required substantial programming effort to uncover--until recently. Our research team has spent over 20 years developing kinship links to genetically inform biometrical modeling. In the most recent release of kinship links from two of the NLSY datasets, the direct kinship indicators included in the 2006 surveys allowed successful and unambiguous linking of over 94 % of the potential pairs. In this paper, we provide details for research teams interested in using the NLSY data portfolio to conduct BG (and other family-oriented) research.
	Bibliography Citation Rodgers, Joseph Lee, William H. Beasley, David E. Bard, Kelly M. Meredith, Michael D. Hunter, Amber Johnson, Maury Allen Buster and Chengchang Li. "The NLSY Kinship Links: Using the NLSY79 and NLSY-Children Data to Conduct Genetically-Informed and Family-Oriented Research." Behavior Genetics 46,4 (July 2016): 538-551.

7.	Rodgers, Joseph Lee Garrison, S. Mason O'Keefe, Patrick Bard, David E. Hunter, Michael D. Beasley, William H. van den Oord, Edwin J. C. G.
7.	Responding to a 100-Year-Old Challenge from Fisher: A Biometrical Analysis of Adult Height in the NLSY Data Using Only Cousin Pairs Behavior Genetics 49,5 (September 2019): 444-454. https://link.springer.com/article/10.1007/s10519-019-09967-6 Cohort(s): Children of the NLSY79, NLSY79 Young Adult Publisher: Behavior Genetics Association Keyword(s): Family, Extended; Height; Kinship Permission to reprint the abstract has not been received from the publisher. In 1918, Fisher suggested that his research team had consistently found inflated cousin correlations. He also commented that because a cousin sample with minimal selection bias was not available the cause of the inflation could not be addressed, leaving this inflation as a challenge still to be solved. In the National Longitudinal Survey of Youth (the NLSY79, the NLSY97, and the NLSY-Children/Young Adult datasets), there are thousands of available cousin pairs. Those in the NLSYC/YA are obtained approximately without selection. In this paper, we address Fisher's challenge using these data. Further, we also evaluate the possibility of fitting ACE models using only cousin pairs, including full cousins, half-cousins, and quarter-cousins. To have any chance at success in such a restricted kinship domain requires an available and highly-reliable phenotype; we use adult height in our analysis. Results provide a possible answer to Fisher's challenge, and demonstrate the potential for using cousin pairs in a stand-alone analysis (as well as in combination with other biometrical designs).
	Bibliography Citation Rodgers, Joseph Lee, S. Mason Garrison, Patrick O'Keefe, David E. Bard, Michael D. Hunter, William H. Beasley and Edwin J. C. G. van den Oord. "Responding to a 100-Year-Old Challenge from Fisher: A Biometrical Analysis of Adult Height in the NLSY Data Using Only Cousin Pairs." Behavior Genetics 49,5 (September 2019): 444-454. https://link.springer.com/article/10.1007/s10519-019-09967-6.

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