Family-based Designs Research Articles

KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.

Genome-wide association studies have problems due to confounding: Are family-based designs the answer?

Genome-wide association studies (GWASs) can be affected by confounding. Family-based GWAS uses random, within-family genetic variation to avoid this. A study in PLOS Biology details how different sources of confounding affect GWAS and whether family-based designs offer a solution.

Exome Sequencing to Identify Novel Variants Associated with Secondary Amenorrhea and Premature Ovarian Insufficiency (POI) in Saudi Women.

Post-pubertal disappearance of menstrual cycles (secondary amenorrhea) associated with premature follicular depletion is a heterogeneous condition. Patients with this disease have low levels of gonadal hormones and high levels of gonadotropins. It is one of the causes of female infertility and a strong genetic component is attributed as an underlying cause of this condition. Although variants in several genes have been associated with the condition, the cause of the disease remains undetermined in the vast majority of cases. Methodology and Materials: Ten Saudi married women experiencing secondary amenorrhea were referred to a center for genetics and inherited diseases for molecular investigation. A family-based study design was used. Intensive clinical examinations, including pelvic ultra-sonography (U/S) and biochemical evaluations, were carried out. Karyotypes were normal in all cases and polycystic ovarian syndrome (PCOS) was excluded by using Rotterdam consensus criteria. Patients' DNA samples were whole-exome sequenced (WES). Bidirectional Sanger sequencing was then utilized to validate the identified candidate variants. The pathogenicity of detected variants was predicted using several types of bioinformatics software. Most of the patients have a normal uterus with poor ovarian reserves. Exome sequence data analysis identified candidate variants in genes associated with POI in 60% of cases. Novel variants were identified in HS6ST1, MEIOB, GDF9, and BNC1 in POI-associated genes. Moreover, a homozygous variant was also identified in the MMRN1 gene. Interestingly, mutations in MMRN1 have never been associated with any human disease. The variants identified in this study were not present in 125 healthy Saudi individuals. WES is a powerful tool to identify the underlying variants in genetically heterogeneous diseases like secondary amenorrhea and POI. In this study, we identified six novel variants and expanded the genotype continuum of POI. Unravelling the genetic landscape of POI will help in genetic counselling, management, and early intervention.

Studying the relationship between intelligence quotient and schizophrenia polygenic scores in a family design with first-episode psychosis population.

The intelligence quotient (IQ) of patients with first-episode psychosis (FEP) and their unaffected relatives may be related to the genetic burden of schizophrenia (SCZ). The polygenic score approach can be useful for testing this question. To assess the contribution of the polygenic risk scores for SCZ (PGS-SCZ) and polygenic scores for IQ (PGS-IQ) to the individual IQ and its difference from the mean IQ of the family (named family-IQ) through a family-based design in an FEP sample. The PAFIP-FAMILIES sample (Spain) consists of 122 FEP patients, 131 parents, 94 siblings, and 176 controls. They all completed the WAIS Vocabulary subtest for IQ estimation and provided a DNA sample. We calculated PGS-SCZ and PGS-IQ using the continuous shrinkage method. To account for relatedness in our sample, we performed linear mixed models. We controlled for covariates potentially related to IQ, including age, years of education, sex, and ancestry principal components. FEP patients significantly deviated from their family-IQ. FEP patients had higher PGS-SCZ than other groups, whereas the relatives had intermediate scores between patients and controls. PGS-IQ did not differ between groups. PGS-SCZ significantly predicted the deviation from family-IQ, whereas PGS-IQ significantly predicted individual IQ. PGS-SCZ discriminated between different levels of genetic risk for the disorder and was specifically related to patients' lower IQ in relation to family-IQ. The genetic background of the disorder may affect neurocognition through complex pathological processes interacting with environmental factors that prevent the individual from reaching their familial cognitive potential.

Prenatal and adult exposure to smoking and incidence of type 1 diabetes in children and adults – a nationwide cohort study with a family-based design

Prenatal exposure to smoking is linked to a reduced risk of type 1 diabetes in children. We wanted to find out if the risk of adult-onset type 1 diabetes is reduced in individuals who are exposed to smoking prenatally or during adulthood. We linked Swedish, nationwide registers and prospectively analyzed incidence of type 1 diabetes in relation to maternal smoking during pregnancy and adult smoking. Everyone was followed until age 30 or year 2019. We employed cohort and sibling design and used adjusted Cox regression and conditional logistic regression. For analyses of maternal smoking there were 3,170,386 individuals (18,745 cases of type 1 diabetes) and for adult smoking 1,608,291 individuals (1274 cases). Prenatal exposure to smoking was associated with lower incidence of type 1 diabetes during childhood and young adulthood (age 20-24, Hazard ratio (HR) 0.76, 95% Confidence interval 0.67-0.87), but not at higher ages. The HR associated with adult smoking was estimated at 1.14 (CI 1.00-1.31) overall and 1.34 (CI 1.03-1.75) in those with family history of diabetes. In sibling analyses, the odds ratio (OR) of type 1 diabetes in relation to prenatal exposure was 0.71 (CI 0.62-0.81) in children and 1.06 (CI 0.75-1.51) in adults (age 19-30), while adult smoking conferred an OR of 1.59 (CI 1.08-2.35). These findings indicate that a reduced risk conferred by tobacco exposure is limited to the prenatal period and type 1 diabetes developing during childhood. Adult smoking may be a risk factor for adult-onset type 1 diabetes, especially in people with family history of diabetes. Swedish Research Councils, Swedish Diabetes and Novo Nordisk Foundations, China Scholarship Council.

Bayesian linear mixed model with multiple random effects for family-based genetic studies

Motivation: Family-based study design is one of the popular designs used in genetic research, and the whole-genome sequencing data obtained from family-based studies offer many unique features for risk prediction studies. They can not only provide a more comprehensive view of many complex diseases, but also utilize information in the design to further improve the prediction accuracy. While promising, existing analytical methods often ignore the information embedded in the study design and overlook the predictive effects of rare variants, leading to a prediction model with sub-optimal performance.Results: We proposed a Bayesian linear mixed model for the prediction analysis of sequencing data obtained from family-based studies. Our method can not only capture predictive effects from both common and rare variants, but also easily accommodate various disease model assumptions. It uses information embedded in the study design to form surrogates, where the predictive effects from unmeasured/unknown genetic and environmental risk factors can be modelled. Through extensive simulation studies and the analysis of sequencing data obtained from the Michigan State University Twin Registry study, we have demonstrated that the proposed method outperforms commonly adopted techniques.Availability: R package is available at https://github.com/yhai943/FBLMM.

A novel rare variants association test for binary traits in family-based designs via copulas.

With the cost-effectiveness technology in whole-genome sequencing, more sophisticated statistical methods for testing genetic association with both rare and common variants are being investigated to identify the genetic variation between individuals. Several methods which group variants, also called gene-based approaches, are developed. For instance, advanced extensions of the sequence kernel association test, which is a widely used variant-set test, have been proposed for unrelated samples and extended for family data. Family data have been shown to be powerful when analyzing rare variants. However, most of such methods capture familial relatedness using a random effect component within the generalized linear mixed model framework. Therefore, there is a need to develop unified and flexible methods to study the association between a set of genetic variants and a trait, especially for a binary outcome. Copulas are multivariate distribution functions with uniform margins on the interval and they provide suitable models to capture familial dependence structure. In this work, we propose a flexible family-based association test for both rare and common variants in the presence of binary traits. The method, termed novel rare variant association test (NRVAT), uses a marginal logistic model and a Gaussian Copula. The latter is employed to model the dependence between relatives. An analytic score-type test is derived. Through simulations, we show that our method can achieve greater power than existing approaches. The proposed model is applied to investigate the association between schizophrenia and bipolar disorder in a family-based cohort consisting of 17 extended families from Eastern Quebec.

Family studies: A useful tool to better understand spondyloarthritis.

Spondyloarthritis (SpA) is an immune-mediated disease characterized by a high heritability, reflected by strong familial aggregation. Therefore, family studies are a powerful tool for elucidating the genetic basis of SpA. First, they helped to assess the relative importance of genetic and environmental factors and established the polygenic character of the disease. Family-based designs were also historically used to identify genetic factors of susceptibility through linkage analyses. In SpA, three whole-genome linkage studies were published in the 1990's, unfortunately with few consistent results. After having been put aside for several years in favour of case-control GWAS, there is a renewed interest in family-based designs in particular to detect rare variant associations. This review aims at summarizing what family studies have brought to the field of SpA genetics, from genetic epidemiology studies to the most recent rare variant analyses. It also highlights the potential interest of family history of SpA to help diagnosis and detection of patients at high risk to develop the disease.

Association between cumulative psychosocial adversity in the family and ADHD and autism: a family-based cohort study

Cumulative exposure to psychosocial adversity at an early age has been shown to be a risk factor for attention-deficit hyperactivity disorder (ADHD) and autism that often co-occur. However, it is not clear if this association reflects a causal effect or familial confounding. We aimed to assess whether cumulative psychosocial adversity in the family increases the risk for ADHD and autism in offspring while accounting for unmeasured familial confounding. We used a population-based cohort of 1,877,901 individuals born in Sweden between 1990 and 2009. Participants were followed from the age of 3 until 2013, with a median follow up time of 13.8 years. We created a cumulative index based on 7 psychosocial adversity factors. We used Cox regression to estimate the hazard ratios (HRs) relating neurodevelopmental conditions to cumulative psychosocial adversity. To address familial confounding, the analyses were repeated in groups of relatives of different kinship: siblings and half-siblings and cousins. A dose-response relationship was observed between cumulative exposure to psychosocial adversity and ADHD at a general population level (covariate adjusted HRs (aHRs) with 95% confidence intervals ranged from 1.55 [one adversity; 1.53–1.58] to 2.65 [ ≥ 4 adversities; 1.98–3.54]). No clear dose-response relation was seen for autism (aHRs ranged from 1.04 [.59–1.84] to 1.37 [1.30–1.45]). HRs of ADHD and autism decreased with increasing level of kinship in the analysis of relatives. Cumulative exposure to psychosocial adversity was associated with both ADHD and autism in the general population, these associations were partly explained by unmeasured familial confounding between relatives. This highlights the need for using family-based designs in studies of psychosocial adversity and ADHD and autism.

Associations Between Attention-Deficit/Hyperactivity Disorder (ADHD), ADHD Medication, and Shorter Height: A Quasi-Experimental and Family-Based Study

The association between attention-deficit/hyperactivity disorder (ADHD) and shorter height is unclear. This study examined the risk of shorter height in individuals with ADHD, and the influence of prenatal factors, ADHD medication, psychiatric comorbidity, socioeconomic factors, and familial liability. We drew on Swedish National Registers for 2 different study designs. First, height data for 14,268 individuals with ADHD and 71,339 controls were stratified into 2 groups: (1) before stimulant treatment was introduced in Sweden, and (2) after stimulant treatment was introduced in Sweden. Second, we used a family-based design including 833,172 relatives without ADHD with different levels of relatedness to the individuals with ADHD and matched controls. ADHD was associated with shorter height both before (below-average height: OR= 1.31, 95% CI= 1.22-1.41) and after (below-average height: OR= 1.21, 95% CI= 1.13-1.31) stimulants for ADHD were introduced in Sweden, and was of similar magnitude in both cohorts. The association between ADHD and shorter height attenuated after adjustment for prenatal factors, psychiatric disorders, and socioeconomic status. Relatives of individuals with ADHD had an increased risk of shorter height (below-average height in full siblings: OR= 1.14, 95% CI= 1.09-1.19; maternal half siblings: OR= 1.10, 95% CI= 1.01-1.20; paternal half siblings: OR= 1.15, 95% CI= 1.07-1.24, first full cousins: OR= 1.10, 95% CI= 1.08-1.12). Our findings suggest that ADHD is associated with shorter height. On a population level, this association was present both before and after ADHD medications were available in Sweden. The association between ADHD and height was partly explained by prenatal factors, psychiatric comorbidity, low socioeconomic status, and a shared familial liability for ADHD.

Something old, something new - can adding genomic data to family studies advance our understanding of the impact of nature and nurture on mental health? Commentary on McAdams et al. (2023).

In their annual research review, McAdams, Cheesman, and Ahmadzadeh (2023) provide a thorough overview of how the use of novel genetically informative approaches can increase our knowledge about the intergenerational transmission of psychopathology. Many JCPP readers will already be familiar with genetically sensitive family-based designs, such as twin and adoption studies, as well as with newer molecular-genetic approaches, such as polygenic-score studies. McAdams et al.'s (2023) review discusses the innovative combination of family-based and molecular-genetic methods, and what this combination can reveal about developmental psychopathology.

Phenome-wide association study of loci harboring de novo tandem repeat mutations in UK Biobank exomes

When present in coding regions, tandem repeats (TRs) may have large effects on protein structure and function contributing to health and disease. We use a family-based design to identify de novo TRs and assess their impact at the population level in 148,607 European ancestry participants from the UK Biobank. The 427 loci with de novo TR mutations are enriched for targets of microRNA-184 (21.1-fold, P = 4.30 × 10−5, FDR = 9.50 × 10−3). There are 123 TR-phenotype associations with posterior probabilities > 0.95. These relate to body structure, cognition, and cardiovascular, metabolic, psychiatric, and respiratory outcomes. We report several loci with large likely causal effects on tissue microstructure, including the FAN1-[TG]N and carotid intima-media thickness (mean thickness: beta = 5.22, P = 1.22 × 10−6, FDR = 0.004; maximum thickness: beta = 6.44, P = 1.12 × 10−6, FDR = 0.004). Two exonic repeats FNBP4-[GGT]N and BTN2A1-[CCT]N alter protein structure. In this work, we contribute clear and testable hypotheses of dose-dependent TR implications linking genetic variation and protein structure with health and disease outcomes.

How to estimate heritability: a guide for genetic epidemiologists.

Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of methods that use large samples of (unrelated or related) genotyped individuals. Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability, i.e. the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals [linkage disequilibrium score regression, genomic relatedness restricted maximum-likelihood (GREML) estimation] and family-based genomic designs (sibling regression, GREML-kinship, trio-genome-wide complex trait analysis, maternal-genome-wide complex trait analysis, relatedness disequilibrium regression). We describe how heritability is estimated for each method and the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared with samples of related individuals. Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why.

Annual Research Review: Towards a deeper understanding of nature and nurture: combining family-based quasi-experimental methods with genomic data.

Distinguishing between the effects of nature and nurture constitutes a major research goal for those interested in understanding human development. It is known, for example, that many parent traits predict mental health outcomes in children, but the causal processes underlying such associations are often unclear. Family-based quasi-experimental designs such as sibling comparison, adoption and extended family studies have been used for decades to distinguish the genetic transmission of risk from the environmental effects family members potentially have on one another. Recently, these designs have been combined with genomic data, and this combination is fuelling a range of exciting methodological advances. In this review we explore these advances - highlighting the ways in which they have been applied to date and considering what they are likely to teach us in the coming years about the aetiology and intergenerational transmission of psychopathology.

KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design

Family-based designs can eliminate confounding due to population substructure and can distinguish direct from indirect genetic effects, but these designs are underpowered due to limited sample sizes. Here, we propose KnockoffTrio, a statistical method to identify putative causal genetic variants for father-mother-child trio design built upon a recently developed knockoff framework in statistics. KnockoffTrio controls the false discovery rate (FDR) in the presence of arbitrary correlations among tests and is less conservative and thus more powerful than the conventional methods that control the family-wise error rate via Bonferroni correction. Furthermore, KnockoffTrio is not restricted to family-based association tests and can be used in conjunction with more powerful, potentially nonlinear models to improve the power of standard family-based tests. We show, using empirical simulations, that KnockoffTrio can prioritize causal variants over associations due to linkage disequilibrium and can provide protection against confounding due to population stratification. In applications to 14,200 trios from three study cohorts for autism spectrum disorders (ASDs), including AGP, SPARK, and SSC, we show that KnockoffTrio can identify multiple significant associations that are missed by conventional tests applied to the same data. In particular, we replicate known ASD association signals with variants in several genes such as MACROD2, NRXN1, PRKAR1B, CADM2, PCDH9, and DOCK4 and identify additional associations with variants in other genes including ARHGEF10, SLC28A1, ZNF589, and HINT1 at FDR 10%.

Intergenerational continuity of crime: A comparison between children of discordant siblings.

Various studies have shown that fathers and children show similarities in criminal behaviour, but little is known about the nature of this relationship. By using a family-based research design, this study controls for familial confounders and gives a better estimate of the extent to which paternal crime has a direct effect on offspring offending. To test the extent of any relationship between paternal offending during the childhood of offspring and adolescent offending by those offspring and to examine the effect of potential confounders of this relationship. Data were from records held by Statistics Netherlands for 1,155,771 individuals born in the Netherlands between 1996 and 2001. Police data were used to measure paternal offending during the childhood (age 0-11) of this cohort and their adolescent offending (age 12-18). Logistic regression analyses were used to estimate the bivariate relationship between paternal and offspring offending, as well as this relationship after controlling for various demographic and socio-economic variables. Conditional logistic regression analyses were used to compare children of discordant brothers (N=9232). By comparing within families rather than between unrelated individuals, all unmeasured familial factors that are shared between these cousins were controlled for. Offending during adolescence was about three times as likely among offspring whose fathers had offended while they were 11years old or younger than among adolescents with no such paternal problem (OR: 3.21, CI 3.17-3.26). This relationship was attenuated after controlling for measured confounders (OR: 1.78, CI 1.75-1.81) and for unmeasured familial confounders (OR: 1.47, CI 1.36-1.59), but remained significant. Paternal offending has an association with offspring offending, but this is small after controlling for measured socio-economic and unmeasured familial confounders. Previous studies that did not control for unmeasured familial confounders seem likely to have overestimated the effect of paternal crime on their offspring's offending. This has implications for interventions for the offspring. If confined to mitigating the negative consequences of paternal offending, they are likely to have limited effectiveness.

Maternal smoking during pregnancy and type 1 diabetes in the offspring: a nationwide register-based study with family-based designs

BackgroundMaternal smoking during pregnancy was reported to be associated with a reduced risk of type 1 diabetes in the offspring. We investigated whether this association is consistent with a causal interpretation by accounting for familial (shared genetic and environmental) factors using family-based, quasi-experimental designs.MethodsWe included 2,995,321 children born in Sweden between 1983 and 2014 and followed them for a diagnosis of type 1 diabetes until 2020 through the National Patient, Diabetes and Prescribed Drug Registers. Apart from conducting a traditional cohort study, we performed a nested case–control study (quasi-experiment) comparing children with type 1 diabetes to their age-matched siblings (or cousins). Information on maternal smoking during pregnancy was retrieved from the Swedish Medical Birth Register. Multivariable adjusted Cox proportional hazards regression and conditional logistic regression were used.ResultsA total of 18,617 children developed type 1 diabetes, with a median age at diagnosis of 9.4 years. The sibling and cousin comparison design included 14,284 and 7988 of these children, respectively. Maternal smoking during pregnancy was associated with a 22% lower risk of offspring type 1 diabetes in the full cohort (hazard ratio 0.78, 95% confidence interval [CI] 0.75–0.82). The corresponding odds ratio was 0.78 (95% CI 0.69–0.88) in the sibling and 0.72 (95% CI 0.66–0.79) in the cousin comparison analysis.ConclusionsThis nationwide, family-based study provides support for a protective effect of maternal smoking on offspring type 1 diabetes. Mechanistic studies are needed to elucidate the underlying pathways behind this link.

Clotting factor genes are associated with preeclampsia in high-altitude pregnant women in the Peruvian Andes

Preeclampsia is a multi-organ complication of pregnancy characterized by sudden hypertension and proteinuria that is among the leading causes of preterm delivery and maternal morbidity and mortality worldwide. The heterogeneity of preeclampsia poses a challenge for understanding its etiology and molecular basis. Intriguingly, risk for the condition increases in high-altitude regions such as the Peruvian Andes. To investigate the genetic basis of preeclampsia in a population living at high altitude, we characterized genome-wide variation in a cohort of preeclamptic and healthy Andean families (n= 883) from Puno, Peru, a city located above 3,800 meters of altitude. Our study collected genomic DNA and medical records from case-control trios and duos in local hospital settings. We generated genotype data for 439,314 SNPs, determined global ancestry patterns, and mapped associations between genetic variants and preeclampsia phenotypes. A transmission disequilibrium test (TDT) revealed variants near genes of biological importance for placental and blood vessel function. The top candidate region was found on chromosome 13 of the fetal genome and contains clotting factor genes PROZ, F7, and F10. These findings provide supporting evidence that common genetic variants within coagulation genes play an important role in preeclampsia. A selection scan revealed a potential adaptive signal around the ADAM12 locus on chromosome 10, implicated in pregnancy disorders. Our discovery of an association in a functional pathway relevant to pregnancy physiology in an understudied population of Native American origin demonstrates the increased power of family-based study design and underscores the importance of conducting genetic research in diverse populations.

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies.

Genetic association studies of child health outcomes often employ family‐based study designs. One of the most popular family‐based designs is the case–parent trio design that considers the smallest possible nuclear family consisting of two parents and their affected child. This trio design is particularly advantageous for studying relatively rare disorders because it is less prone to type 1 error inflation due to population stratification compared to population‐based study designs (e.g., case–control studies). However, obtaining genetic data from both parents is difficult, from a practical perspective, and many large studies predominantly measure genetic variants in mother–child dyads. While some statistical methods for analyzing parent‐child dyad data (most commonly involving mother–child pairs) exist, it is not clear if they provide the same advantage as trio methods in protecting against population stratification, or if a specific dyad design (e.g., case–mother dyads vs. case–mother/control–mother dyads) is more advantageous. In this article, we review existing statistical methods for analyzing genome‐wide marker data on dyads and perform extensive simulation experiments to benchmark their type I errors and statistical power under different scenarios. We extend our evaluation to existing methods for analyzing a combination of case–parent trios and dyads together. We apply these methods on genotyped and imputed data from multiethnic mother–child pairs only, case–parent trios only or combinations of both dyads and trios from the Gene, Environment Association Studies consortium (GENEVA), where each family was ascertained through a child affected by nonsyndromic cleft lip with or without cleft palate. Results from the GENEVA study corroborate the findings from our simulation experiments. Finally, we provide recommendations for using statistical genetic association methods for dyads.