Sample Of Unrelated Individuals Research Articles

Exploration of Genome-Wide Recombination Rate Variation Patterns at Different Scales in Pigs.

Meiotic recombination is a prevalent process in eukaryotic sexual reproduction organisms that plays key roles in genetic diversity, breed selection, and species evolution. However, the recombination events differ across breeds and even within breeds. In this study, we initially computed large-scale population recombination rates of both sexes using approximately 52 K SNP genotypes in a total of 3279 pigs from four different Chinese and Western breeds. We then constructed a high-resolution historical recombination map using approximately 16 million SNPs from a sample of unrelated individuals. Comparative analysis of porcine recombination events from different breeds and at different resolutions revealed the following observations: Firstly, the 1Mb-scale pig recombination maps of the same sex are moderately conserved among different breeds, with the similarity of recombination events between Western pigs and Chinese indigenous pigs being lower than within their respective groups. Secondly, we identified 3861 recombination hotspots in the genome and observed medium- to high-level correlation between historical recombination rates (0.542~0.683) and estimates of meiotic recombination rates. Third, we observed that recombination hotspots are significantly far from the transcription start sites of pig genes, and the silico-predicted PRDM9 zinc finger domain DNA recognition motif is significantly enriched in the regions of recombination hotspots compared to recombination coldspots, highlighting the potential role of PRDM9 in regulating recombination hotspots in pigs. Our study analyzed the variation patterns of the pig recombination map at broad and fine scales, providing a valuable reference for genomic selection breeding and laying a crucial foundation for further understanding the molecular mechanisms of pig genome recombination.

The influence of biological relatedness on sexual dimorphism and sex classification based on external morphology of the frontal bone.

The most significant sexual differences in the human skull are located in the upper third of the face (the frontal bone), which is a useful research object, mainly in combination with virtual anthropology methods. However, the influence of biological relatedness on sexual dimorphism and frontal bone variability remains unknown. This study was directed at sexual difference description and sex classification using the form and shape of the external surface of the frontal bones from a genealogically documented Central European osteological sample (nineteenth to twentieth centuries). The study sample consisted of 47 cranial CT images of the adult members of several branches of one family group over 4 generations. Three-dimensional virtual models of the frontal bones were analyzed using geometric morphometrics and multidimensional statistics. Almost the entire external frontal surface was significantly different between males and females, especially in form. Significant differences were also found between this related sample and an unrelated one. Sex estimation of the biologically related individuals was performed using the classification models developed on a sample of unrelated individuals from the recent Czech population (Čechová et al. in Int J Legal Med 133: 1285 1294, 2019), with a result of 74.46% and 63.83% in form and shape, respectively. Failure of this classifier was caused by the existence of typical traits found in the biologically related sample different from the usual manifestation of sexual dimorphism. This can be explained as due to the increased degree of similarity and the reduction of variability in biologically related individuals. The results show the importance of testing previously published methods on genealogical data.

Body mass index and childhood symptoms of depression, anxiety, and attention-deficit hyperactivity disorder: A within-family Mendelian randomization study.

Higher BMI in childhood is associated with emotional and behavioural problems, but these associations may not be causal. Results of previous genetic studies imply causal effects but may reflect influence of demography and the family environment. This study used data on 40,949 8-year-old children and their parents from the Norwegian Mother, Father and Child Cohort Study (MoBa) and Medical Birth Registry of Norway (MBRN). We investigated the impact of BMI on symptoms of depression, anxiety, and attention-deficit hyperactivity disorder (ADHD) at age 8. We applied within-family Mendelian randomization, which accounts for familial effects by controlling for parental genotype. Within-family Mendelian randomization estimates using genetic variants associated with BMI in adults suggested that a child's own BMI increased their depressive symptoms (per 5 kg/m2 increase in BMI, beta = 0.26 S.D., CI = -0.01,0.52, p=0.06) and ADHD symptoms (beta = 0.38 S.D., CI = 0.09,0.63, p=0.009). These estimates also suggested maternal BMI, or related factors, may independently affect a child's depressive symptoms (per 5 kg/m2 increase in maternal BMI, beta = 0.11 S.D., CI:0.02,0.09, p=0.01). However, within-family Mendelian randomization using genetic variants associated with retrospectively-reported childhood body size did not support an impact of BMI on these outcomes. There was little evidence from any estimate that the parents' BMI affected the child's ADHD symptoms, or that the child's or parents' BMI affected the child's anxiety symptoms. We found inconsistent evidence that a child's BMI affected their depressive and ADHD symptoms, and little evidence that a child's BMI affected their anxiety symptoms. There was limited evidence of an influence of parents' BMI. Genetic studies in samples of unrelated individuals, or using genetic variants associated with adult BMI, may have overestimated the causal effects of a child's own BMI. This research was funded by the Health Foundation. It is part of the HARVEST collaboration, supported by the Research Council of Norway. Individual co-author funding: the European Research Council, the South-Eastern Norway Regional Health Authority, the Research Council of Norway, Helse Vest, the Novo Nordisk Foundation, the University of Bergen, the South-Eastern Norway Regional Health Authority, the Trond Mohn Foundation, the Western Norway Regional Health Authority, the Norwegian Diabetes Association, the UK Medical Research Council. The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit.

An exploration of linkage fine-mapping on sequences from case-control studies.

Linkage analysis maps genetic loci for a heritable trait by identifying genomic regions with excess relatedness among individuals with similar trait values. Analysis may be conducted on related individuals from families, or on samples of unrelated individuals from a population. For allelically heterogeneous traits, population-based linkage analysis can be more powerful than genotypic-association analysis. Here, we focus on linkage analysis in a population sample, but use sequences rather than individuals as our unit of observation. Earlier investigations of sequence-based linkage mapping relied on known sequence relatedness, whereas we infer relatedness from the sequence data. We propose two ways to associate similarity in relatedness of sequences with similarity in their trait values and compare the resulting linkage methods to two genotypic-association methods. We also introduce a procedure to label case sequences as potential carriers or noncarriers of causal variants after an association has been found. This post hoc labeling of case sequences is based on inferred relatedness to other case sequences. Our simulation results indicate that methods based on sequence relatedness improve localization and perform as well as genotypic-association methods for detecting rare causal variants. Sequence-based linkage analysis therefore has potential to fine-map allelically heterogeneous disease traits.

Evaluating indirect genetic effects of siblings using singletons.

Estimating effects of parental and sibling genotypes (indirect genetic effects) can provide insight into how the family environment influences phenotypic variation. There is growing molecular genetic evidence for effects of parental phenotypes on their offspring (e.g. parental educational attainment), but the extent to which siblings affect each other is currently unclear. Here we used data from samples of unrelated individuals, without (singletons) and with biological full-siblings (non-singletons), to investigate and estimate sibling effects. Indirect genetic effects of siblings increase (or decrease) the covariance between genetic variation and a phenotype. It follows that differences in genetic association estimates between singletons and non-singletons could indicate indirect genetic effects of siblings if there is no heterogeneity in other sources of genetic association between singletons and non-singletons. We used UK Biobank data to estimate polygenic score (PGS) associations for height, BMI and educational attainment in self-reported singletons (N = 50,143) and non-singletons (N = 328,549). The educational attainment PGS association estimate was 12% larger (95% C.I. 3%, 21%) in the non-singleton sample than in the singleton sample, but the height and BMI PGS associations were consistent. Birth order data suggested that the difference in educational attainment PGS associations was driven by individuals with older siblings rather than firstborns. The relationship between number of siblings and educational attainment PGS associations was non-linear; PGS associations were 24% smaller in individuals with 6 or more siblings compared to the rest of the sample (95% C.I. 11%, 38%). We estimate that a 1 SD increase in sibling educational attainment PGS corresponds to a 0.025 year increase in the index individual's years in schooling (95% C.I. 0.013, 0.036). Our results suggest that older siblings may influence the educational attainment of younger siblings, adding to the growing evidence that effects of the environment on phenotypic variation partially reflect social effects of germline genetic variation in relatives.

SimRVSequences: an R package to simulate genetic sequence data for pedigrees.

We present the R package SimRVSequences to simulate sequence data for pedigrees. SimRVSequences allows for simulations of large numbers of single-nucleotide variants (SNVs) and scales well with increasing numbers of pedigrees. Users provide a sample of pedigrees and SNV data from a sample of unrelated individuals. SimRVSequences is publicly-available on CRAN https://cran.r-project.org/web/packages/SimRVSequences/. Supplementary data are available at Bioinformatics online.

Within family Mendelian randomization studies.

Mendelian randomization (MR) is increasingly used to make causal inferences in a wide range of fields, from drug development to etiologic studies. Causal inference in MR is possible because of the process of genetic inheritance from parents to offspring. Specifically, at gamete formation and conception, meiosis ensures random allocation to the offspring of one allele from each parent at each locus, and these are unrelated to most of the other inherited genetic variants. To date, most MR studies have used data from unrelated individuals. These studies assume that genotypes are independent of the environment across a sample of unrelated individuals, conditional on covariates. Here we describe potential sources of bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effects and assortative mating that can induce spurious or biased SNP-phenotype associations. We explain how studies of related individuals such as sibling pairs or parent-offspring trios can be used to overcome some of these sources of bias, to provide potentially more reliable evidence regarding causal processes. The increasing availability of data from related individuals in large cohort studies presents an opportunity to both overcome some of these biases and also to evaluate familial environmental effects.

Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

Characterization of mitochondrial DNA polymorphisms in the Han population in Liaoning Province, Northeast China

This study characterized the genetic variations of mitochondrial DNA (mtDNA) to elucidate the maternal genetic structure of Liaoning Han Chinese. A total of 317 blood samples of unrelated individuals were collected for analysis in Liaoning Province. The mtDNA samples were analyzed using two distinct methods: sequencing of the hypervariable sequences I and II (HVSI and HVSII), and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis of the coding region. The results indicated a high gene diversity value (0.9997 ± 0.0003), a high polymorphism information content (0.99668) and a random match probability (0.00332). These samples were classified into 305 haplotypes, with 9 shared haplotypes. The most common haplogroup was D4 (12.93%). The principal component analysis map, the phylogenetic tree map, and the genetic distance matrix all indicated that the genetic distance of the Liaoning Han population from the Tibetan group was distant, whereas that from the Miao group was relatively close.

An association between TLR1 and TLR6 gene polymorphism and development of bronchial asthma

The aim of this study was to analyze an association between TLR1 and TLR6 gene polymorphism and development of bronchial asthma in Russian patients living at the Republic of Bashkortostan. Methods . We investigated DNA samples of unrelated individuals (n = 179) and healthy volunteers (n = 121) of Russian ethnic group. DNA was extracted by phenol-chlorophorm extraction. Genotyping of polymorphic variant was performed by the real-time polymerase chain reaction. Results . An association between rs5743571 polymorphism in the TLR1 gene and rs5743794 polymorphism in the TLR6 gene with occurrence of bronchial asthma has been found. Conclusion. TLR1 and TLR6 genes are important for asthma susceptibility in Russian population. Polymorphism in rs5743571*C allele and rs5743571*CC genotype of TLR1 gene could be used as high-risk markers; rs5743571*CT genotype of this polymorphic variant could be used as low-risk marker of asthma occurrence. These results can improve our knowledge on defensive mechanisms against atopic sensitization including TLR2 heterodimers and subsequent development of atopic asthma.

Are Epigenetic Factors Implicated in Chronic Widespread Pain?

BackgroundChronic widespread musculoskeletal pain (CWP) is the cardinal symptom of fibromyalgia and affects about 12% of the general population. Familial aggregation of CWP has been repeatedly demonstrated with estimated heritabilities of around 50%, indicating a genetic susceptibility. The objective of the study was to explore genome-wide disease-differentially methylated positions (DMPs) for chronic widespread pain (CWP) in a sample of unrelated individuals and a subsample of discordant monozygotic (MZ) twins.Methodology/Principle FindingsA total of N = 281 twin individuals from the TwinsUK registry, including N = 33 MZ twins discordant for self-reported CWP, were part of the discovery sample. The replication sample included 729 men and 756 women from a subsample of the KORA S4 survey–an independent population-based cohort from Southern Germany. Epigenome-wide analysis of DNA methylation was conducted using the Illumina Infinium HumanMethylation 450 DNA BeadChip in both the discovery and replication sample. Of our 40 main loci that were carried forward for replication, three CPGs reached significant p-values in the replication sample, including malate dehydrogenase 2 (MDH2; p-value 0.017), tetranectin (CLEC3B; p-value 0.039), and heat shock protein beta-6 (HSPB6; p-value 0.016). The associations between the collagen type I, alpha 2 chain (COL1A2) and monoamine oxidase B (MAOB) observed in the discovery sample–both of which have been previously reported to be biological candidates for pain–could not be replicated.Conclusion/SignificanceOur results may serve as a starting point to encourage further investigation in large and independent population-based cohorts of DNA methylation and other epigenetic changes as possible disease mechanisms in CWP. Ultimately, understanding the key mechanisms underlying CWP may lead to new treatments and inform clinical practice.

Discovery of copy number variants by multiplex amplifiable probe hybridization (MAPH) in candidate pigmentation genes

Background: Copy Number Variants (CNVs) contribute to a large fraction of genetic diversity and some of them have been reported to offer an evolutionary advantage.Aim: To identify CNVs in pigmentary loci that could contribute to human skin pigmentation diversity.Subjects and methods: This study assessed the existence of CNVs in every exon of candidate genes: TYR, TYRP1, DCT, MC1R and SLC24A5, using the Multiplex Amplifiable Probe Hybridization technique (MAPH). This study analysed a total of 99 DNA samples of unrelated individuals from different populations. Validation and further analysis in a larger Spanish sample were performed by RT-qPCR.Results: Five CNVs were identified by MAPH: DCT exons 4 and 8, TYR exon 1 and SLC24A5 exons 1 and 4. Real-time quantitative PCR (RT-qPCR) confirmed the CNV in exon 1 of SLC24A5. This study further analysed the 5′ promoter region of SLC24A5 and found another CNV in this region. However, no association was found between the CNV and the degree of pigmentation.Conclusion: Although the functional role of these structural variants in pigmentation should be the subject of future work, the results emphasize the need to consider all classes of variation (both SNPs and CNVs) when exploring the genetics of skin pigmentation.

H09 The Depression Phenotype In Huntington's Disease

<h3>Background</h3> Depression is one of the most common psychiatric disorders in individuals with Huntington’s disease (HD) and negatively impacts quality of life, functional ability and cognitive performance. However, reported prevalence rates vary greatly and little is known about the presentation of depression in HD. <h3>Aims</h3> To determine the lifetime prevalence of DSM-IV depressive disorders in a sample of unrelated individuals with HD, and to compare the depression phenotype in this HD sample and individuals with unipolar depression without HD. <h3>Methods</h3> Fifty unrelated individuals gene positive for HD were assessed for lifetime psychopathology using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) and best-estimate lifetime DSM-IV diagnosis and key illness course features were determined. 784 individuals with DSM-IV major recurrent depressive disorder (MDDR) and no HD, who were assessed using the same methods in an ongoing genetic epidemiology study, formed the comparison group. <h3>Results</h3> Twenty-eight individuals (56%) in the HD sample had a lifetime diagnosis of a DSM-IV depressive disorder and 12 individuals (24%) had a lifetime diagnosis of MDDR. Compared to the non-HD sample, individuals with HD and MDDR were significantly more likely to have a later median age at onset of depression (p = 0.001) and a higher level of functioning during their worst episode of depression (p = 0.006) and were significantly less likely to experience diurnal variation (p = 0.008), middle insomnia (p = 0.036) and increased appetite (p = 0.043) during their worst episode of depression. <h3>Conclusions</h3> The prevalence of lifetime DSM-IV depressive disorders in HD is up to four times higher than in the general population and the depression phenotype may be less severe with a higher age of onset.

Substantial SNP-based heritability estimates for working memory performance.

Working memory (WM) is an important endophenotype in neuropsychiatric research and its use in genetic association studies is thought to be a promising approach to increase our understanding of psychiatric disease. As for any genetically complex trait, demonstration of sufficient heritability within the specific study context is a prerequisite for conducting genetic studies of that trait. Recently developed methods allow estimating trait heritability using sets of common genetic markers from genome-wide association study (GWAS) data in samples of unrelated individuals. Here we present single-nucleotide polymorphism (SNP)-based heritability estimates (h2SNP) for a WM phenotype. A Caucasian sample comprising a total of N=2298 healthy and young individuals was subjected to an N-back WM task. We calculated the genetic relationship between all individuals on the basis of genome-wide SNP data and performed restricted maximum likelihood analyses for variance component estimation to derive the h2SNP estimates. Heritability estimates for three 2-back derived WM performance measures based on all autosomal chromosomes ranged between 31 and 41%, indicating a substantial SNP-based heritability for WM traits. These results indicate that common genetic factors account for a prominent part of the phenotypic variation in WM performance. Hence, the application of GWAS on WM phenotypes is a valid method to identify the molecular underpinnings of WM.

Analysis of 15 autosomal STR loci from Mar del Plata and Bahia Blanca (Central Region of Argentina)

Allele frequencies for the 15 short tandem repeats (STRs) loci included in the AmpFlSTR® Identifiler kit were estimated in a sample of unrelated individuals from Mar del Plata (MDQ; N = 180) and Bahia Blanca (BB; N = 85) (Buenos Aires, Argentina). Biological samples were obtained from voluntary donors and forensic cases. Both populations were in Hardy-Weinberg equilibrium after Bonferroni correction, except for locus vWA in MDQ and D2S1338 in BB. FGA was the most informative locus, and the least discriminating locus was TPOX in both samples. The combined power of discrimination (PDc) and the combined probability of exclusion (PEc) were similar in MDQ and BB samples (0.999999998 < PDc < 0.999999999 and 0.999999979 < PEc < 0.999999989). The multidimentional scaling plot from Rst genetic distance matrix and the interethnic admixture estimation supported a higher European contribution in populations of the central region compared with populations from other regions of Argentina with higher Amerindian composition. These results enlarge the Argentine databases of autosomal STR loci, revealed as an excellent tool for human identification tests and population genetic analysis.

Intelligence indexes generalist genes for cognitive abilities

Twin research has supported the concept of intelligence (general cognitive ability, g) by showing that genetic correlations between diverse tests of verbal and nonverbal cognitive abilities are greater than 0.50. That is, most of the genes that affect cognitive abilities are highly pleiotropic in the sense that genes that affect one cognitive ability affect all cognitive abilities. The impact of this finding may have been blunted because it depends on the validity of the twin method. Although the assumptions of the twin method have survived indirect tests, it is now possible to test findings from the twin method directly using DNA alone in samples of unrelated individuals, without the assumptions of the twin method. We applied this DNA method, implemented in a software package called Genome-wide Complex Trait Analysis (GCTA), to estimate genetic variance and covariance for two verbal tests and two nonverbal tests using 1.7million DNA markers genotyped on 2500 unrelated children at age 12; 1900 children also had cognitive data and DNA at age 7. Because each of these individuals is one member of a twin pair, we were able to compare GCTA estimates directly to twin study estimates using the same measures in the same sample. At age 12, GCTA confirmed the results of twin research in showing substantial genetic covariance between verbal and nonverbal composites. The GCTA genetic correlation at age 12 was 1.0 (SE=0.32), not significantly different from the twin study estimate of 0.60 (SE=0.09). At age 7, the genetic correlations were 0.31 (SE=0.32) from GCTA and 0.71 (SE=0.15).from twin analysis. The results from the larger sample and stronger measures at age 12 confirm the twin study results that the genetic architecture of intelligence is driven by pleiotropic effects on diverse cognitive abilities. However, the results at age 7 and the large standard errors of GCTA bivariate genetic correlations suggest the need for further research with larger samples.

No Genetic Influence for Childhood Behavior Problems From DNA Analysis

ObjectiveTwin studies of behavior problems in childhood point to substantial genetic influence. It is now possible to estimate genetic influence using DNA alone in samples of unrelated individuals, not relying on family-based designs such as twins. A linear mixed model, which incorporates DNA microarray data, has confirmed twin results by showing substantial genetic influence for diverse traits in adults. Here we present direct comparisons between twin and DNA heritability estimates for childhood behavior problems as rated by parents, teachers, and children themselves.MethodBehavior problem data from 2,500 UK-representative 12-year-old twin pairs were used in twin analyses; DNA analyses were based on 1 member of the twin pair with genotype data for 1.7 million DNA markers. Diverse behavior problems were assessed, including autistic, depressive, and hyperactive symptoms. Genetic influence from DNA was estimated using genome-wide complex trait analysis (GCTA), and the twin estimates of heritability were based on standard twin model fitting.ResultsBehavior problems in childhood—whether rated by parents, teachers, or children themselves—show no significant genetic influence using GCTA, even though twin study estimates of heritability are substantial in the same sample, and even though both GCTA and twin study estimates of genetic influence are substantial for cognitive and anthropometric traits.ConclusionsWe suggest that this new type of “missing heritability,” that is, the gap between GCTA and twin study estimates for behavior problems in childhood, is due to nonadditive genetic influence, which will make it more difficult to identify genes responsible for heritability.

Genetics of Callous-Unemotional Behavior in Children

Callous-unemotional behavior (CU) is currently under consideration as a subtyping index for conduct disorder diagnosis. Twin studies routinely estimate the heritability of CU as greater than 50%. It is now possible to estimate genetic influence using DNA alone from samples of unrelated individuals, not relying on the assumptions of the twin method. Here we use this new DNA method (implemented in a software package called Genome-wide Complex Trait Analysis, GCTA) for the first time to estimate genetic influence on CU. We also report the first genome-wide association (GWA) study of CU as a quantitative trait. We compare these DNA results to those from twin analyses using the same measure and the same community sample of 2,930 children rated by their teachers at ages 7, 9 and 12. GCTA estimates of heritability were near zero, even though twin analysis of CU in this sample confirmed the high heritability of CU reported in the literature, and even though GCTA estimates of heritability were substantial for cognitive and anthropological traits in this sample. No significant associations were found in GWA analysis, which, like GCTA, only detects additive effects of common DNA variants. The phrase ‘missing heritability’ was coined to refer to the gap between variance associated with DNA variants identified in GWA studies versus twin study heritability. However, GCTA heritability, not twin study heritability, is the ceiling for GWA studies because both GCTA and GWA are limited to the overall additive effects of common DNA variants, whereas twin studies are not. This GCTA ceiling is very low for CU in our study, despite its high twin study heritability estimate. The gap between GCTA and twin study heritabilities will make it challenging to identify genes responsible for the heritability of CU.

DNA Evidence for Strong Genome-Wide Pleiotropy of Cognitive and Learning Abilities

Very different neurocognitive processes appear to be involved in cognitive abilities such as verbal and non-verbal ability as compared to learning abilities taught in schools such as reading and mathematics. However, twin studies that compare similarity for monozygotic and dizygotic twins suggest that the same genes are largely responsible for genetic influence on these diverse aspects of cognitive function. It is now possible to test this evidence for strong pleiotropy using DNA alone from samples of unrelated individuals. Here we used this new method with 1.7 million DNA markers for a sample of 2,500 unrelated children at age 12 to investigate for the first time the extent of pleiotropy between general cognitive ability (aka intelligence) and learning abilities (reading, mathematics and language skills). We also compared these DNA results to results from twin analyses using the same sample and measures. The DNA-based method revealed strong genome-wide pleiotropy: Genetic correlations were greater than 0.70 between general cognitive ability and language, reading, and mathematics, results that were highly similar to twin study estimates of genetic correlations. These results indicate that genes related to diverse neurocognitive processes have general rather than specific effects.

Evaluation of methods accounting for population structure with pedigree data and continuous outcomes

Methods to account for population structure (PS) in genome-wide association studies have been well developed in samples of unrelated individuals, but when a sample is composed of families, the task of finding and accounting for PS is not as straight forward. Family-based tests that condition on parental genotypes or their sufficient statistics are immune to biases due to PS, but are known to have low power, particularly for unselected samples. Population-based approaches that use all available data are an attractive alternative, but the methods have not been evaluated for continuous outcomes when a sample has both family and PS. Therefore, we compare through simulation the performance of population-based regression models that account for family and PS with continuous outcomes using a range of family sizes and structures, including two and three generational families with admixed and discrete PS. We find that when computation time is a concern, the Dupuis et al. efficient score test performs very well. When computational time is not an issue, a linear mixed effects model adjusting for genetic principal components tends to have slightly better power than the score test and may be preferred.