Eating Disorders: From Twin Studies to Candidate Genes and Beyond

AbstractSubstantial effort has been put into the exploration of the biological background of eating disorders, through family, twin and molecular genetic studies. Family studies have shown that anorexia (AN) and bulimia nervosa (BN) are strongly familial, and that familial etiologic factors appear to be shared by both disorders. Twin studies often focus on broader phenotypes or subthreshold eating disorders. These studies consistently yielded moderate to substantial heritabilities. In addition, there has been a proliferation of molecular genetic studies that focused on Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) AN and BN. Seven linkage regions have been identified in genome-wide screens. Many genetic association studies have been performed, but no consistent association between a candidate gene and AN or BN has been reported. Larger genetic association studies and collaborations are needed to examine the involvement of several candidate genes and biological pathways in eating disorders. In addition, twin studies should be designed to assist the molecular work by further exploring genetic determinants of endophenotypes, evaluating the magnitude of contribution to liability of measured genotypes as well as environmental risk factors related to eating disorders. In this manner twin and molecular studies can move the field forward in a mutually informative way.

Download Full-text

Genetics of Pediatric Irritability

Irritability in Pediatric Psychopathology ◽

10.1093/med-psych/9780190846800.003.0008 ◽

2019 ◽

pp. 149-170

Author(s):

Meridith L. Eastman ◽

Ashlee A. Moore ◽

Roxann Roberson-Nay

Keyword(s):

Environmental Factors ◽

Candidate Genes ◽

Association Studies ◽

Molecular Genetic ◽

Human Studies ◽

Genome Wide Association Studies ◽

Genetic Studies ◽

Genome Wide ◽

Literature Searches ◽

Selection Of

This chapter provides an overview of behavioral and molecular genetics of pediatric irritability. Literature searches using PubMed and PsycInfo databases yielded 37 relevant animal and human studies on irritability. Studies of rodent and primate models initially suggested a genetic etiology for the trait and influenced selection of candidate genes for study in human studies. Behavioral genetic studies of irritability suggest that pediatric irritability is likely influenced by additive genetic and nonshared unique environmental factors, with little to no influence of dominant genetic or shared family environmental factors. Molecular genetic studies have been largely limited to candidate genes with a few emerging genome-wide association studies (GWAS). Results from the candidate gene literature on irritability are inconclusive, and GWAS in clinical populations has yielded limited findings. Future genetic studies of irritability would benefit from the use of appropriate phenotypic measures, adequate sample sizes, and multimethod and longitudinal approaches.

Download Full-text

Evaluation of genome-wide power of genetic association studies based on empirical data from the HapMap project

Human Molecular Genetics ◽

10.1093/hmg/ddm205 ◽

2007 ◽

Vol 16 (20) ◽

pp. 2494-2505 ◽

Cited By ~ 23

Author(s):

Yasuhito Nannya ◽

Kenjiro Taura ◽

Mineo Kurokawa ◽

Shigeru Chiba ◽

Seishi Ogawa

Keyword(s):

Genetic Association ◽

Empirical Data ◽

Association Studies ◽

Genetic Association Studies ◽

Hapmap Project ◽

Genome Wide

Download Full-text

Insights into Dyslexia Genetics Research from the Last Two Decades

Brain Sciences ◽

10.3390/brainsci12010027 ◽

2021 ◽

Vol 12 (1) ◽

pp. 27

Author(s):

Florina Erbeli ◽

Marianne Rice ◽

Silvia Paracchini

Keyword(s):

Language Disorder ◽

Association Studies ◽

Neurodevelopmental Disorder ◽

Molecular Genetic ◽

Genetic Research ◽

Twin Studies ◽

Genome Wide Association Studies ◽

Genetics Research ◽

Genome Wide ◽

Genetic Risks

Dyslexia, a specific reading disability, is a common (up to 10% of children) and highly heritable (~70%) neurodevelopmental disorder. Behavioral and molecular genetic approaches are aimed towards dissecting its significant genetic component. In the proposed review, we will summarize advances in twin and molecular genetic research from the past 20 years. First, we will briefly outline the clinical and educational presentation and epidemiology of dyslexia. Next, we will summarize results from twin studies, followed by molecular genetic research (e.g., genome-wide association studies (GWASs)). In particular, we will highlight converging key insights from genetic research. (1) Dyslexia is a highly polygenic neurodevelopmental disorder with a complex genetic architecture. (2) Dyslexia categories share a large proportion of genetics with continuously distributed measures of reading skills, with shared genetic risks also seen across development. (3) Dyslexia genetic risks are shared with those implicated in many other neurodevelopmental disorders (e.g., developmental language disorder and dyscalculia). Finally, we will discuss the implications and future directions. As the diversity of genetic studies continues to increase through international collaborate efforts, we will highlight the challenges in advances of genetics discoveries in this field.

Download Full-text

Random Forests for Genetic Association Studies

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1691 ◽

2011 ◽

Vol 10 (1) ◽

Cited By ~ 85

Author(s):

Benjamin A Goldstein ◽

Eric C Polley ◽

Farren B. S. Briggs

Keyword(s):

Machine Learning ◽

Genetic Association ◽

Random Forests ◽

Learning Algorithm ◽

Association Studies ◽

Genetic Association Studies ◽

Machine Learning Algorithms ◽

Computationally Efficient ◽

Genetic Studies ◽

Variable Importance Measures

The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.

Download Full-text

Efficient estimation of disease odds ratios for follow-up genetic association studies

Statistical Methods in Medical Research ◽

10.1177/0962280217741771 ◽

2017 ◽

Vol 28 (7) ◽

pp. 1927-1941

Author(s):

Jiyuan Hu ◽

Wei Zhang ◽

Xinmin Li ◽

Dongdong Pan ◽

Qizhai Li

Keyword(s):

Genetic Association ◽

Association Studies ◽

Genetic Association Studies ◽

Efficient Estimation ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Odds Ratios ◽

Genome Wide ◽

Follow Up Studies

In the past decade, genome-wide association studies have identified thousands of susceptible variants associated with complex human diseases and traits. Conducting follow-up genetic association studies has become a standard approach to validate the findings of genome-wide association studies. One problem of high interest in genetic association studies is to accurately estimate the strength of the association, which is often quantified by odds ratios in case-control studies. However, estimating the association directly by follow-up studies is inefficient since this approach ignores information from the genome-wide association studies. In this article, an estimator called GFcom, which integrates information from genome-wide association studies and follow-up studies, is proposed. The estimator includes both the point estimate and corresponding confidence interval. GFcom is more efficient than competing estimators regarding MSE and the length of confidence intervals. The superiority of GFcom is particularly evident when the genome-wide association study suffers from severe selection bias. Comprehensive simulation studies and applications to three real follow-up studies demonstrate the performance of the proposed estimator. An R package, “GFcom”, implementing our method is publicly available at https://github.com/JiyuanHu/GFcom .

Download Full-text

Genome-Wide Control of Population Structure and Relatedness in Genetic Association Studies via Linear Mixed Models with Orthogonally Partitioned Structure

10.1101/409953 ◽

2018 ◽

Author(s):

Matthew P. Conomos ◽

Alex P. Reiner ◽

Mary Sara McPeek ◽

Timothy A. Thornton

Keyword(s):

Population Structure ◽

Genetic Association ◽

Mixed Models ◽

Association Studies ◽

Linear Mixed Models ◽

Genetic Association Studies ◽

European Ancestry ◽

Type I ◽

Genome Wide ◽

Wbc Count

AbstractLinear mixed models (LMMs) have become the standard approach for genetic association testing in the presence of sample structure. However, the performance of LMMs has primarily been evaluated in relatively homogeneous populations of European ancestry, despite many of the recent genetic association studies including samples from worldwide populations with diverse ancestries. In this paper, we demonstrate that existing LMM methods can have systematic miscalibration of association test statistics genome-wide in samples with heterogenous ancestry, resulting in both increased type-I error rates and a loss of power. Furthermore, we show that this miscalibration arises due to varying allele frequency differences across the genome among populations. To overcome this problem, we developed LMM-OPS, an LMM approach which orthogonally partitions diverse genetic structure into two components: distant population structure and recent genetic relatedness. In simulation studies with real and simulated genotype data, we demonstrate that LMM-OPS is appropriately calibrated in the presence of ancestry heterogeneity and outperforms existing LMM approaches, including EMMAX, GCTA, and GEMMA. We conduct a GWAS of white blood cell (WBC) count in an admixed sample of 3,551 Hispanic/Latino American women from the Women’s Health Initiative SNP Health Association Resource where LMM-OPS detects genome-wide significant associations with corresponding p-values that are one or more orders of magnitude smaller than those from competing LMM methods. We also identify a genome-wide significant association with regulatory variant rs2814778 in the DARC gene on chromosome 1, which generalizes to Hispanic/Latino Americans a previous association with reduced WBC count identified in African Americans.

Download Full-text

Mortality Selection in a Genetic Sample and Implications for Association Studies

10.1101/049635 ◽

2016 ◽

Cited By ~ 5

Author(s):

Benjamin W. Domingue ◽

Daniel W. Belsky ◽

Amal Harrati ◽

Dalton Conley ◽

David Weir ◽

...

Keyword(s):

Data Collection ◽

Birth Cohort ◽

Genetic Association ◽

Selection Process ◽

Association Studies ◽

Genetic Association Studies ◽

Genomic Data ◽

Mortality Selection ◽

Snp Data ◽

Genome Wide

AbstractMortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection—the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection—has been largely ignored. Here we test the hypothesis that such mortality selection may significantly alter estimates in polygenetic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide SNP data collection at older ages using the U.S.-based Health and Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the genetic sample of the HRS. Mortality selection leads to modest drift in estimating time-varying genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection. There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how genetic association studies using HRS data can adjust for mortality selection from study entry to time of genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in genetically-informed samples from other cohort studies.

Download Full-text