Search Results

Author: Bard, David E.
Resulting in 15 citations.
1. Bard, David E.
Modeling Age-of-Onset in Behavior Genetic Substance Use Research: It's About Time?
Presented: Storrs, CT, Behavior Genetics Association 2006 Annual Meeting, June 20-25, 2006
Cohort(s): NLSY79
Publisher: Behavior Genetics Association
Keyword(s): Alcohol Use; Behavior; Cigarette Use (see Smoking); Event History; Genetics; Kinship; Modeling, Biometric; Siblings

Permission to reprint the abstract has not been received from the publisher.

A brief history of age-at-onset modeling in behavior genetics was presented followed by a new discrete-time survival method for estimating ACE variance components of genetically informative age-at-onset data. The new method was framed as an adaptation of the Goldstein, [Pan, Bynner (2004)] multilevel model for event histories. Extensions of the model for multivariate outcomes were also discussed. Using this new technique, univariate and multivariate behavior genetic models of alcohol and cigarette initiation were fit to responses from adolescents of the National Longitudinal Survey of Youth (NLSY). This nationally representative sample produced results consistent with prior behavior genetic research on both substances [e.g., Madden et al (1999) Koopmans et al (1999) Stallings et al (1999)]. Initiation of either substance appeared to be predominantly influenced by the environmental sources of variation, with little to no support for additive genetic influences. Despite this similarity, results supported the investigation of both initiations separately, as substantial ACE unique effects were present in the MV model. Lastly, estimates of shared environmental effects from this study were consistently lower than those present in most previous investigations. This can likely be attributed both to the larger variety of genetic relatedness existing in this kinship, as opposed twin-only, sample, as well as the greater variability in age-at-onset measured initiation, as opposed to status indicators (e.g., used, never used). Pros and cons of this more detailed initiation phenotype were discussed in the context of past, present, and future substance use theory and research.
Bibliography Citation
Bard, David E. "Modeling Age-of-Onset in Behavior Genetic Substance Use Research: It's About Time?" Presented: Storrs, CT, Behavior Genetics Association 2006 Annual Meeting, June 20-25, 2006.
2. Bard, David E.
Hunter, Michael D.
Beasley, William H.
Rodgers, Joseph Lee
Meredith, Kelly M.
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.
3. Bard, David E.
Rodgers, Joseph Lee
Sibling Influence on Smoking Behavior: A Within-Family Look at Explanations for a Birth-Order Effect
Journal of Applied Social Psychology 33,9 (September 2003): 1773-1795.
Also: http://onlinelibrary.wiley.com/doi/10.1111/j.1559-1816.2003.tb02080.x/abstract
Cohort(s): NLSY79
Publisher: Blackwell Publishing, Inc. => Wiley Online
Keyword(s): Birth Order; Cigarette Use (see Smoking); Family Models; Family Studies; Siblings

Using a repeated-measures design, we found a significant birth-order relationship suggesting lower ages of smoking onset in later born siblings of a 1979 National Longitudinal Survey of Youth cohort. Two social learning mechanisms, modeling and opportunity, were explored to help illuminate the causes of trends in the within-family means. When empirical patterns were compared to predictions derived from our specifications of how opportunity and modeling processes should work, the results were unsuccessful in explaining the birth-order effect. As a third explanation of the birth-order effect, telescoping did show a significant influence. The effect size was small, however, and had little effect on the group means assessed. Finally, a pattern did emerge that was consistent with a reformulation of the opportunity process in which sisters play a particularly strong role. We develop future research implications of this pattern and speculate on genetic and social conservatism explanations. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Bard, David E. and Joseph Lee Rodgers. "Sibling Influence on Smoking Behavior: A Within-Family Look at Explanations for a Birth-Order Effect." Journal of Applied Social Psychology 33,9 (September 2003): 1773-1795.
4. Bard, David E.
Rodgers, Joseph Lee
Use of Discrete-time Survival Analysis for Modeling Multivariate ACE Models of Fertility Precursors from the Children of the NLSY
Presented: Storrs, CT, Behavior Genetics Association Annual Conference, 36th Annual, June 2006.
Also: http://www.bga.org//meetings/2006/Abstracts.pdf
Cohort(s): Children of the NLSY79, NLSY79, NLSY79 Young Adult
Publisher: Behavior Genetics Association
Keyword(s): Age at First Intercourse; Age at Menarche/First Menstruation; Fertility; Genetics; Intergenerational Patterns/Transmission; Siblings

Permission to reprint the abstract has not been received from the publisher.

Substantial evidence now exists that variables measuring or correlated with fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility precursors and fit a multivariate ACE model to responses from the children (now adolescents and young adults) born to mothers of the original National Longitudinal Survey of Youth (NLSY) cohort. Three age-related precursors were considered: age at 1st menstruation, 1st dating experience, and 1st sexual intercourse. Univariate and multivariate models were in general agreement indicating strong heritability for each precursor, little to no shared environmental influences, and small to moderate nonshared influences. Genetic components in the MV model accounted for 47%, 71%, and 54% of the precursor variations, respectively. Methodologically, this study also explored the use of MV random effect discrete-time survival analyses of the precursor data. These models also incorporated an additional precursor (age at 1st marriage) and a fertility outcome (age at 1st childbirth). Results from these 5-variable discrete-time survival models are compared to biased effects from models that excluded censored cases.
Bibliography Citation
Bard, David E. and Joseph Lee Rodgers. "Use of Discrete-time Survival Analysis for Modeling Multivariate ACE Models of Fertility Precursors from the Children of the NLSY." Presented: Storrs, CT, Behavior Genetics Association Annual Conference, 36th Annual, June 2006.
5. Beasley, William H.
Bard, David E.
Hunter, Michael D.
Meredith, Kelly M.
Rodgers, Joseph Lee
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.
6. Hunter, Michael D.
Bard, David E.
Beasley, William H.
Meredith, Kelly M.
Rodgers, Joseph Lee
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.
7. Miller, Warren B.
Bard, David E.
Pasta, David J.
Rodgers, Joseph Lee
Biodemographic Modeling of the Links Between Fertility Motivation and Fertility Outcomes in the NLSY79
Demography 47,2 (May 2010): 393-414.
Also: http://muse.jhu.edu/login?uri=/journals/demography/v047/47.2.miller.html
Cohort(s): NLSY79
Publisher: Population Association of America
Keyword(s): Childbearing; Fertility; Gender Attitudes/Roles; Genetics; LISREL; Modeling; Modeling, Multilevel

Permission to reprint the abstract has not been received from the publisher.

In spite of long-held beliefs that traits related to reproductive success tend to become fixed by evolution with little or no genetic variation, there is now considerable evidence that the natural variation of fertility within populations is genetically influenced and that a portion of that influence is related to the motivational precursors to fertility. We conduct a two-stage analysis to examine these inferences in a time-ordered multivariate context. First, using data from the National Longitudinal Survey of Youth, 1979, and LISREL analysis, we develop a structural equation model in which five hypothesized motivational precursors to fertility, measured in 1979-1982, predict both a child-timing and a child-number outcome, measured in 2002. Second, having chosen two time-ordered sequences of six variables from the SEM to represent our phenotypic models, we use Mx to conduct both univariate and multivariate behavioral genetic analyses with the selected variables. Our results indicate that one or more genes acting within a gene network have additive effects that operate through childnumber desires to affect both the timing of the next child born and the final number of children born, that one or more genes acting through a separate network may have additive effects operating through gender role attitudes to produce downstream effects on the two fertility outcomes, and that no genetic variance is associated with either child-timing intentions or educational intentions. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Miller, Warren B., David E. Bard, David J. Pasta and Joseph Lee Rodgers. "Biodemographic Modeling of the Links Between Fertility Motivation and Fertility Outcomes in the NLSY79." Demography 47,2 (May 2010): 393-414.
8. Rodgers, Joseph Lee
Bard, David E.
Behavior Genetics and Adolescent Development: A Review of Recent Literature
In: Blackwell Handbook of Adolescence. G. Adams and M. Berzonsky, eds., Malden, MA: Wiley-Blackwell, June 2003.
Also: http://www.blackwellpublishing.com/content/BPL_Images/Content_store/Sample_chapter/063121920X/001.pdf
Cohort(s): Children of the NLSY79
Publisher: Blackwell Publishing, Inc. => Wiley Online
Keyword(s): Adolescent Behavior; Behavior Problems Index (BPI); Family Environment; Family Influences; Genetics; Home Observation for Measurement of Environment (HOME); Kinship; Modeling, Biometric; Psychological Effects; Siblings

Introduction to Chapter 1: Behavior genetics is a quantitative method, and adolescent development is a psychological topic. Treating the cross between these two arenas appears, at the surface, to require collecting research in which the method has been applied to study the topic, and reviewing that research for coherence and common themes. But the challenge is rather more difficult than the surface level view might suggest. Below the surface is a great deal of shifting sand, which makes organizing the topic difficult. Because of this instability, it is critical that we carefully and explicitly define a foundational starting point. In the introduction to this article, we begin with some definitions, and then we describe the difficulties inherent in reviewing "behavior genetics and adolescent development." We conclude our introduction with a summary of the foundation on which we will base our review. In the next section, we carefully build that foundation. Following, we summarize the relevant research, and embed it within the organizational foundation.
Bibliography Citation
Rodgers, Joseph Lee and David E. Bard. "Behavior Genetics and Adolescent Development: A Review of Recent Literature" In: Blackwell Handbook of Adolescence. G. Adams and M. Berzonsky, eds., Malden, MA: Wiley-Blackwell, June 2003.
9. Rodgers, Joseph Lee
Bard, David E.
Modeling NLSY Fertility Patterns Longitudinally and Biometrically: Evolutionary, Genetic, and Social Interpretations
Presented: Philadelphia, PA, Population Association of America Annual Meeting, March-April 2005
Cohort(s): NLSY79
Publisher: Population Association of America
Keyword(s): Fertility; Genetics; Kinship; Modeling, Multilevel

Permission to reprint the abstract has not been received from the publisher.

Using fertility patterns and kinship information from the National Longitudinal Survey of Youth (NLSY), we fit biometrical models to partition genetic, shared environmental, and nonshared environmental variance associated with fertility differences in the NLSY females. Those females -- who were aged 35-42 in the 2000 data -- have mostly completed childbearing. Our model is a multivariate longitudinal model linking early fertility, early middle fertility, middle fertility, and late middle fertility. Our analysis shows different genetic sources underlying early and later fertility, and strong shared environmental influences only for early fertility. These findings are interpreted in relation to Fisher's Fundamental Theorem of Natural Selection, and also in relation to a theory developed by Udry (1995) explaining how the amount of reproductive choice constrains the link between fertility preferences and the biological expression of those preferences.
Bibliography Citation
Rodgers, Joseph Lee and David E. Bard. "Modeling NLSY Fertility Patterns Longitudinally and Biometrically: Evolutionary, Genetic, and Social Interpretations." Presented: Philadelphia, PA, Population Association of America Annual Meeting, March-April 2005.
10. Rodgers, Joseph Lee
Bard, David E.
Johnson, Amber
D'Onofrio, Brian M.
Miller, Warren B.
The Cross-Generational Mother–Daughter–Aunt–Niece Design: Establishing Validity of the MDAN Design with NLSY Fertility Variables
Behavior Genetics 38,6 (November 2008): 567-578.
Also: http://www.springerlink.com/content/x75521h0l957w296/
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Behavior Genetics Association
Keyword(s): Behavior; Fertility; Genetics; Inheritance; Kinship; Mothers and Daughters; Siblings

Permission to reprint the abstract has not been received from the publisher.

Using National Longitudinal Survey of Youth (NLSY) fertility variables, we introduce and illustrate a new genetically-informative design. First, we develop a kinship linking algorithm, using the NLSY79 and the NLSY-Children data to link mothers to daughters and aunts to nieces. Then we construct mother–daughter correlations to compare to aunt–niece correlations, an MDAN design, within the context of the quantitative genetic model. The results of our empirical illustration, which uses DF Analysis and generalized estimation equations (GEE) to estimate biometrical parameters from NLSY79 sister–sister pairs and their children in the NLSY-Children dataset, provide both face validity and concurrent validity in support of the efficacy of the design. We describe extensions of the MDAN design. Compared to the typical within-generational design used in most behavior genetic research, the cross-generational feature of this design has certain advantages and interesting features. In particular, we note that the equal environment assumption of the traditional biometrical model shifts in the context of a cross-generational design. These shifts raise questions and provide motivation for future research using the MDAN and other cross-generational designs. [ABSTRACT FROM AUTHOR]

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Bibliography Citation
Rodgers, Joseph Lee, David E. Bard, Amber Johnson, Brian M. D'Onofrio and Warren B. Miller. "The Cross-Generational Mother–Daughter–Aunt–Niece Design: Establishing Validity of the MDAN Design with NLSY Fertility Variables." Behavior Genetics 38,6 (November 2008): 567-578.
11. Rodgers, Joseph Lee
Bard, David E.
Miller, Warren B.
Mother-Daughter-Aunt-Niece (MDAN) Design, Applied to Cross-Generational NLSY
Presented: Storrs, CT, Behavior Genetics Association, 36th Annual Annual Conference, June 2006
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Behavior Genetics Association
Keyword(s): Age at First Intercourse; Age at Menarche/First Menstruation; Genetics; Intergenerational Patterns/Transmission; Mothers and Daughters; Self-Reporting

Permission to reprint the abstract has not been received from the publisher.

A new biometrical design – called the MDAN design – emerges from the complex longitudinal survey design of the National Longitudinal Survey of Youth (NLSY) data. Using the crossgenerational structure available in the NLSY, we link mothers to daughters and aunts to nieces, creating an MDAN (mother-daughter-aunt-niece) design. The cross-generational data include NLSY-females who are only mothers, those who are only aunts, and those who are both mothers and aunts. Further, there is within-generational biometrical information linking NLSY-Youth females to one another as cousins, half-siblings, full-siblings, and twins; and linking NLSYChildren females to one another as cousins, half siblings, full siblings, and twins. We create linking files identifying the various within- and between-generational links, and fit preliminary biometrical models using those links. Phenotypes are fertility variables, typically measured across the two generations at approximately the same age and using identical measurement instruments. Specific measures on which we focus include self-reported age at menarche and self-reported age at first intercourse. Previous research using biometrical models have studied these phenotypes within each generation; the current research substantially extends both the empirical results and the methodological innovation by taking advantage of the ability to fit three different types of genetically- and environmentally-informed structure simultaneously.
Bibliography Citation
Rodgers, Joseph Lee, David E. Bard and Warren B. Miller. "Mother-Daughter-Aunt-Niece (MDAN) Design, Applied to Cross-Generational NLSY." Presented: Storrs, CT, Behavior Genetics Association, 36th Annual Annual Conference, June 2006.
12. Rodgers, Joseph Lee
Bard, David E.
Miller, Warren B.
Multivariate Cholesky Models of Human Female Fertility Patterns in the NLSY
Behavior Genetics 37,2 (March 2007): 345-361.
Also: http://www.springerlink.com/content/mt8j270588g24168/
Cohort(s): Children of the NLSY79
Publisher: Behavior Genetics Association
Keyword(s): Fertility; Genetics; Life Course; Modeling, Multilevel; Siblings

Permission to reprint the abstract has not been received from the publisher.

Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene–gene and gene–environment interactions for which we must account to better understand individual differences in fertility outcomes.
Bibliography Citation
Rodgers, Joseph Lee, David E. Bard and Warren B. Miller. "Multivariate Cholesky Models of Human Female Fertility Patterns in the NLSY." Behavior Genetics 37,2 (March 2007): 345-361.
13. Rodgers, Joseph Lee
Beasley, William H.
Bard, David E.
Meredith, Kelly M.
Hunter, Michael D.
Johnson, Amber
Buster, Maury Allen
Li, Chengchang
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.
14. 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.
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.
15. Rodgers, Joseph Lee
Johnson, Amber
Bard, David E.
Inferring Sibling Relatedness from the NLSY Youth and Children Data: Past, Present, and Future Prospects
Presented: Los Angeles, CA, Population Association of America (PAA) Annual Meetings, March-April 2006
Cohort(s): Children of the NLSY79
Publisher: Population Association of America
Keyword(s): Genetics; Height; Height, Height-Weight Ratios; Kinship; Modeling, Multilevel; Siblings; Weight

Permission to reprint the abstract has not been received from the publisher.

In a number of disciplinary arenas related to demography, the distinction between full, half, and adoptive siblings is highly relevant. Behavioral genetics requires information about genetic and environmental relatedness to fit biometrical models. Socialization researchers infer expected commitment -- and predicted social learning -- from these sibling categories. Family structure researchers rely on these distinctions as inputs to their models. However, despite its remarkable features and innovations, the National Longitudinal Survey of Youth data do not explicitly distinguish between full, half, and adoptive silbings. We report on our several kinship linking algorithms that infer sibling relatedness from other information in the NLSY and NLSYC. Internal validation procedures use height and weight information, and concurrent validity indicators compare NLSY sibling results to other sibling studies. Past successes and the current status of these efforts are reviewed, and plans to collect explicit sibling information for the NLSY is reported.
Bibliography Citation
Rodgers, Joseph Lee, Amber Johnson and David E. Bard. "Inferring Sibling Relatedness from the NLSY Youth and Children Data: Past, Present, and Future Prospects." Presented: Los Angeles, CA, Population Association of America (PAA) Annual Meetings, March-April 2006.