Single Nucleotide Polymorphism Heritability Research Articles

Investigating the Shared Genetic Architecture Between Psychiatric Disorders and Executive Function

BackgroundEvidence for widespread comorbidity of executive dysfunctions with psychiatric disorders suggests common mechanisms underlying their pathophysiology. However, the shared genetic architectures between psychiatric disorders and executive function (EF) remain poorly understood. MethodsLeveraging large genome-wide association study datasets of European ancestry on bipolar disorder (N = 353,899), major depressive disorder (N = 674,452), and schizophrenia (N = 130,644) from the Psychiatric Genomics Consortium and iPSYCH and a common factor of EF (N = 427,037) from UK Biobank, we systematically investigated the shared genomic architectures between psychiatric disorders and EF with a set of statistical genetic, functional genomic, and gene-level analyses. ResultsOur study demonstrated substantial genetic overlaps and significant genetic correlations between psychiatric disorders and EF. EF showed an estimated 95.9%, 98.1%, and 99.2% of phenotype-influencing variants, as well as 50, 23, and 130 genomic loci shared with bipolar disorder, major depressive disorder, and schizophrenia, respectively. Single nucleotide polymorphism heritability enrichment suggests that the genetic architecture of psychiatric disorders and EF involves the brain’s frontal cortex and prefrontal glutamatergic neurons 1 and 2. Functional genomic analysis of shared variants identified 12 functional regulatory variants that regulate gene expression by affecting the binding affinities of 5 transcription factors. In addition, functional characterization analyses of shared genes revealed potential common biological mechanisms related to synaptic processes and fetal brain development. ConclusionsOur findings provide evidence for extensive shared genetic architectures between psychiatric disorders and EF and have valuable implications for future mechanistic investigations and drug development efforts.

Exploring the Genetic Risk of Childhood Daytime Urinary Incontinence: A Genome-Wide Association Study.

Childhood incontinence is stigmatized and underprioritized, and a basic understanding of its pathogenesis is missing. Our goal was to identify risk-conferring genetic variants in daytime urinary incontinence (DUI). We conducted a genome-wide association study in the Danish iPSYCH2015 cohort. Cases (3024) were identified through DUI diagnosis codes and redeemed prescriptions for DUI medication in children aged 5 to 20. Controls (30,240), selected from the same sample, were matched to cases on sex and psychiatric diagnoses, if any, and down-sampled to a 1:10 case:control ratio. Replication was performed in the Icelandic deCODE cohort (5475 cases/287,773 controls). Single-nucleotide polymorphism heritability was calculated using the genome-based restricted maximum likelihood method. Cross-trait genetic correlation was estimated using linkage disequilibrium score regression. Polygenic risk scores generated with LDpred2-auto and BOLT-LMM were assessed for association. Variants on chromosome 6 (rs12210989, OR = 1.24 [95% CI: 1.17-1.32], P = 3.21 × 10-12) and 20 (rs4809801, OR = 1.18 [95% CI: 1.11-1.25], P = 3.66 × 10-8) reached genome-wide significance and implicated the PRDM13 and RIPOR3 genes. Chromosome 6 findings were replicated (P = .024, OR = 1.09 [95% CI: 1.01-1.16]). Liability scale heritability ranged from 10.20% (95% CI: 6.40%-14.00%) to 15.30% (95% CI: 9.66%-20.94%). DUI and nocturnal enuresis showed positive genetic correlation (rg = 1.28 ± 0.38, P = .0007). DUI was associated with attention-deficit/hyperactivity disorder (OR = 1.098 [95% CI: 1.046-1.152], P < .0001) and body mass index (OR = 1.129 [95% CI: 1.081-1.178], P < .0001) polygenic risk. Common genetic variants contribute to the risk of childhood DUI and genes important in neuronal development and detrusor smooth muscle activity were implicated. These findings may help guide identification of new treatment targets.

Genome-wide association study for age-related hearing loss in CFW mice.

Age-related hearing impairment is the most common cause of hearing loss and is one of the most prevalent conditions affecting the elderly globally. It is influenced by a combination of environmental and genetic factors. The mouse and human inner ears are functionally and genetically homologous. Investigating the genetic basis of age-related hearing loss (ARHL) in an outbred mouse model may lead to a better understanding of the molecular mechanisms of this condition. We used Carworth Farms White (CFW) outbred mice, because they are genetically diverse and exhibit variation in the onset and severity of ARHL. The goal of this study was to identify genetic loci involved in regulating ARHL. Hearing at a range of frequencies was measured using Auditory Brainstem Response (ABR) thresholds in 946 male and female CFW mice at the age of 1, 6, and 10 months. We obtained genotypes at 4.18 million single nucleotide polymorphisms (SNP) using low-coverage (mean coverage 0.27x) whole-genome sequencing followed by imputation using STITCH. To determine the accuracy of the genotypes we sequenced 8 samples at >30x coverage and used calls from those samples to estimate the discordance rate, which was 0.45%. We performed genetic analysis for the ABR thresholds for each frequency at each age, and for the time of onset of deafness for each frequency. The SNP heritability ranged from 0 to 42% for different traits. Genome-wide association analysis identified several regions associated with ARHL that contained potential candidate genes, including Dnah11, Rapgef5, Cpne4, Prkag2, and Nek11. We confirmed, using functional study, that Prkag2 deficiency causes age-related hearing loss at high frequency in mice; this makes Prkag2 a candidate gene for further studies. This work helps to identify genetic risk factors for ARHL and to define novel therapeutic targets for the treatment and prevention of ARHL.

Genome-Wide Association Study of Obsessive-Compulsive Symptoms including 33,943 individuals from the general population.

While 1-2% of individuals meet the criteria for a clinical diagnosis of obsessive-compulsive disorder (OCD), many more (~13-38%) experience subclinical obsessive-compulsive symptoms (OCS) during their life. To characterize the genetic underpinnings of OCS and its genetic relationship to OCD, we conducted the largest genome-wide association study (GWAS) meta-analysis of parent- or self-reported OCS to date (N = 33,943 with complete phenotypic and genome-wide data), combining the results from seven large-scale population-based cohorts from Sweden, the Netherlands, England, and Canada (including six twin cohorts and one cohort of unrelated individuals). We found no genome-wide significant associations at the single-nucleotide polymorphism (SNP) or gene-level, but a polygenic risk score (PRS) based on the OCD GWAS previously published by the Psychiatric Genetics Consortium (PGC-OCD) was significantly associated with OCS (Pfixed = 3.06 × 10-5). Also, one curated gene set (Mootha Gluconeogenesis) reached Bonferroni-corrected significance (Ngenes = 28, Beta = 0.79, SE = 0.16, Pbon = 0.008). Expression of genes in this set is high at sites of insulin mediated glucose disposal. Dysregulated insulin signaling in the etiology of OCS has been suggested by a previous study describing a genetic overlap of OCS with insulin signaling-related traits in children and adolescents. We report a SNP heritability of 4.1% (P = 0.0044) in the meta-analyzed GWAS, and heritability estimates based on the twin cohorts of 33-43%. Genetic correlation analysis showed that OCS were most strongly associated with OCD (rG = 0.72, p = 0.0007) among all tested psychiatric disorders (N = 11). Of all 97 tested phenotypes, 24 showed a significant genetic correlation with OCS, and 66 traits showed concordant directions of effect with OCS and OCD. OCS have a significant polygenic contribution and share genetic risk with diagnosed OCD, supporting the hypothesis that OCD represents the extreme end of widely distributed OCS in the population.

SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

Polygenic Analyses Show Important Differences Between Major Depressive Disorder Symptoms Measured Using Various Instruments

BackgroundSymptoms of major depressive disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire-9 (PHQ-9) (current symptoms) and the Composite International Diagnostic Interview Short-Form (CIDI-SF) (worst-episode symptoms). We performed a systematic comparison between them for their genetic architecture and utility in investigating MDD heterogeneity. MethodsUsing data from the UK Biobank (n = 41,948–109,417), we assessed the single nucleotide polymorphism heritability and genetic correlation (rg) of both sets of MDD symptoms. We further compared their rg with non-MDD traits and used Mendelian randomization to assess whether either set of symptoms has more genetic sharing with non-MDD traits. We also assessed how specific each set of symptoms is to MDD using the metric polygenic risk score pleiotropy. Finally, we used genomic structural equation modeling to identify factors that explain the genetic covariance between each set of symptoms. ResultsCorresponding symptoms reported through the PHQ-9 and CIDI-SF have low to moderate genetic correlations (rg = 0.43–0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip structure in the CIDI-SF. Both Mendelian randomization and polygenic risk score pleiotropy analyses showed that PHQ-9 symptoms are more associated with traits that reflect general dysphoria, whereas the skip structure in the CIDI-SF allows for the identification of heterogeneity among likely MDD cases. Finally, the 2 sets of symptoms showed different factor structures in genomic structural equation modeling, reflective of their genetic differences. ConclusionsMDD symptoms assessed using the PHQ-9 and CIDI-SF are not interchangeable; the former better indexes general dysphoria, while the latter is more informative about within-MDD heterogeneity.

Dissecting shared genetic architecture between obesity and multiple sclerosis.

Observational studies have associated obesity with an increased risk of multiple sclerosis (MS). However, the role of genetic factors in their comorbidity remains largely unknown. Our study aimed to investigate the shared genetic architecture underlying obesity and MS. By leveraging data from genome-wide association studies, we investigated the genetic correlation of body mass index (BMI) and MS by linkage disequilibrium score regression and genetic covariance analyser. The casualty was identified by bidirectional Mendelian randomisation. Linkage disequilibrium score regression in specifically expressed genes and multimarker analysis of GenoMic annotation was utilised to explore single-nucleotide polymorphism (SNP) enrichment at the tissue and cell-type levels. Shared risk SNPs were derived using cross-trait meta-analyses and Heritability Estimation from Summary Statistics. We explored the potential functional genes using summary-data-based Mendelian randomization (SMR). The expression profiles of the risk gene in tissues were further examined. We found a significantly positive genetic correlation between BMI and MS, and the causal association of BMI with MS was supported (β=0.22, P=8.03E-05). Cross-trait analysis yielded 39 shared risk SNPs, and the risk gene GGNBP2 was consistently identified in SMR. We observed tissue-specific level SNP heritability enrichment for BMI mainly in brain tissues for MS in immune-related tissues, and cell-type-specific level SNP heritability enrichment in 12 different immune cell types in brain, spleen, lung, and whole blood. The expressions of GGNBP2 were significantly altered in the tissues of patients with obesity or MS compared to those of control subjects. Our study indicates the genetic correlation and shared risk genes between obesity and MS. These findings provide insights into the potential mechanisms behind their comorbidity and the future development of therapeutics. This work was funded by the National Natural Science Foundation of China (82171698, 82170561, 81300279, and 81741067), the Program for High-level Foreign Expert Introduction of China (G2022030047L), the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province (2021B1515020003), Natural Science Foundation of Guangdong Province (2022A1515012081), the Foreign Distinguished Teacher Program of Guangdong Science and Technology Department (KD0120220129), the Climbing Programme of Introduced Talents and High-level Hospital Construction Project of Guangdong Provincial People's Hospital (DFJH201803, KJ012019099, KJ012021143, and KY012021183), and in part by VA Clinical Merit and ASGE clinical research funds (FWL).

Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses.

Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.

Leveraging genetic overlap between irritability and psychiatric disorders to identify genetic variants of major psychiatric disorders

Irritability is a heritable core mental trait associated with several psychiatric illnesses. However, the genomic basis of irritability is unclear. Therefore, this study aimed to 1) identify the genetic variants associated with irritability and investigate the associated biological pathways, genes, and tissues as well as single-nucleotide polymorphism (SNP)-based heritability; 2) explore the relationships between irritability and various traits, including psychiatric disorders; and 3) identify additional and shared genetic variants for irritability and psychiatric disorders. We conducted a genome-wide association study (GWAS) using 379,506 European samples (105,975 cases and 273,531 controls) from the UK Biobank. We utilized various post-GWAS analyses, including linkage disequilibrium score regression, the bivariate causal mixture model (MiXeR), and conditional and conjunctional false discovery rate approaches. This GWAS identified 15 independent loci associated with irritability; the total SNP heritability estimate was 4.19%. Genetic correlations with psychiatric disorders were most pronounced for major depressive disorder (MDD) and bipolar II disorder (BD II). MiXeR analysis revealed polygenic overlap with schizophrenia (SCZ), bipolar I disorder (BD I), and MDD. Conditional false discovery rate analyses identified additional loci associated with SCZ (number [n] of additional SNPs = 105), BD I (n = 54), MDD (n = 107), and irritability (n = 157). Conjunctional false discovery rate analyses identified 85, 41, and 198 shared loci between irritability and SCZ, BD I, and MDD, respectively. Multiple genetic loci were associated with irritability and three main psychiatric disorders. Given that irritability is a cross-disorder trait, these findings may help to elucidate the genomics of psychiatric disorders.

Genome-wide association study finds multiple loci associated with intraocular pressure in HS rats.

Elevated intraocular pressure (IOP) is influenced by environmental and genetic factors. Increased IOP is a major risk factor for most types of glaucoma, including primary open angle glaucoma (POAG). Investigating the genetic basis of IOP may lead to a better understanding of the molecular mechanisms of POAG. The goal of this study was to identify genetic loci involved in regulating IOP using outbred heterogeneous stock (HS) rats. HS rats are a multigenerational outbred population derived from eight inbred strains that have been fully sequenced. This population is ideal for a genome-wide association study (GWAS) owing to the accumulated recombinations among well-defined haplotypes, the relatively high allele frequencies, the accessibility to a large collection of tissue samples, and the large allelic effect size compared to human studies. Both male and female HS rats (N = 1,812) were used in the study. Genotyping-by-sequencing was used to obtain ∼3.5 million single nucleotide polymorphisms (SNP) from each individual. SNP heritability for IOP in HS rats was 0.32, which agrees with other studies. We performed a GWAS for the IOP phenotype using a linear mixed model and used permutation to determine a genome-wide significance threshold. We identified three genome-wide significant loci for IOP on chromosomes 1, 5, and 16. Next, we sequenced the mRNA of 51 whole eye samples to find cis-eQTLs to aid in identification of candidate genes. We report 5 candidate genes within those loci: Tyr, Ctsc, Plekhf2, Ndufaf6 and Angpt2. Tyr, Ndufaf6 and Angpt2 genes have been previously implicated by human GWAS of IOP-related conditions. Ctsc and Plekhf2 genes represent novel findings that may provide new insight into the molecular basis of IOP. This study highlights the efficacy of HS rats for investigating the genetics of elevated IOP and identifying potential candidate genes for future functional testing.

Heritability of cognitive abilities and regional brain structures in middle-aged to elderly East Asians.

This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer's and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.

Examination of a novel expression-based gene-SNP annotation strategy to identify tissue-specific contributions to heritability in multiple traits.

Complex traits show clear patterns of tissue-specific expression influenced by single nucleotide polymorphisms (SNPs), yet current strategies aggregate SNP effects to genes by employing simple physical proximity-based windows. Here, we examined whether incorporating SNPs with effects on tissue-specific cis-expression would improve our ability to detect trait-relevant tissues across 31 complex traits using stratified linkage disequilibrium score regression (S-LDSC). We found that a physical proximity annotation produced more significant tissue enrichments and larger S-LDSC regression coefficients, as compared to an expression-based annotation. Furthermore, we showed that our expression-based annotation did not outperform an annotation strategy in which an equal number of randomly chosen SNPs were annotated to genes within the same genomic window, suggesting extensive redundancy among SNP effect estimates due to linkage disequilibrium. That said, current sample sizes limit estimation of cis-genetic SNP effects; therefore, we recommend reexamination of the expression-based annotation when larger tissue-specific expression datasets become available. To examine the influence of sample size, we used a large whole blood eQTL reference panel (N = 31,684) applying a similar expression-based annotation strategy. We found that significant cis-expression QTLs in whole blood did not outperform the physical proximity annotation when estimating tissue-specific SNP heritability enrichment for either high- or low-density lipoprotein phenotypes but performed similarly for inflammatory bowel disease. Finally, we report new and updated tissue enrichment estimates across 31 complex traits, such as significant heritability enrichment of the frontal cortex for cognitive performance, educational attainment, and intelligence, providing further evidence of this structure's importance in higher cognitive function.

Using neuroimaging genomics to investigate the evolution of human brain structure

Alterations in brain size and organization represent some of the most distinctive changes in the emergence of our species. Yet, there is limited understanding of how genetic factors contributed to altered neuroanatomy during human evolution. Here, we analyze neuroimaging and genetic data from up to 30,000 people in the UK Biobank and integrate with genomic annotations for different aspects of human evolution, including those based on ancient DNA and comparative genomics. We show that previously reported signals of recent polygenic selection for cortical anatomy are not replicable in a more ancestrally homogeneous sample. We then investigate relationships between evolutionary annotations and common genetic variants shaping cortical surface area and white-matter connectivity for each hemisphere. Our analyses identify single-nucleotide polymorphism heritability enrichment in human-gained regulatory elements that are active in early brain development, affecting surface areas of several parts of the cortex, including left-hemispheric speech-associated regions. We also detect heritability depletion in genomic regions with Neanderthal ancestry for connectivity of the uncinate fasciculus; this is a white-matter tract involved in memory, language, and socioemotional processing with relevance to neuropsychiatric disorders. Finally, we show that common genetic loci associated with left-hemispheric pars triangularis surface area overlap with a human-gained enhancer and affect regulation of ZIC4, a gene implicated in neurogenesis. This work demonstrates how genomic investigations of present-day neuroanatomical variation can help shed light on the complexities of our evolutionary past.

Brain-specific genes contribute to chronic but not to acute back pain.

Back pain is the leading cause of disability worldwide. Although most back pain cases are acute, 20% of acute pain patients experience chronic back pain symptoms. It is unclear whether acute pain and chronic pain have similar or distinct underlying genetic mechanisms. To characterize the molecular and cellular pathways contributing to acute and chronic pain states. Cross-sectional observational genome-wide association study. A total of 375,158 individuals from the UK Biobank cohort were included in the discovery of genome-wide association study. Of those, 70,633 (19%) and 32,209 (9%) individuals met the definition of chronic and acute back pain, respectively. A total of 355 single nucleotide polymorphism grouped into 13 loci reached the genome-wide significance threshold (5x10-8) for chronic back pain, but none for acute. Of these, 7 loci were replicated in the Nord-Trøndelag Health Study (HUNT) cohort (19,760 chronic low back pain cases and 28,674 pain-free controls). Single nucleotide polymorphism heritability was 4.6% (P=1.4x10-78) for chronic back pain and 0.81% (P=1.4x10-8) for acute back pain. Similar differences in heritability estimates between acute and chronic back pain were found in the HUNT cohort: 3.4% (P=0.0011) and 0.6% (P=0.851), respectively. Pathway analyses, tissue-specific heritability enrichment analyses, and epigenetic characterization suggest a substantial genetic contribution to chronic but not acute back pain from the loci predominantly expressed in the central nervous system. Chronic back pain is substantially more heritable than acute back pain. This heritability is mostly attributed to genes expressed in the brain.

Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 × 10-8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.

Novel genetic associations with five aesthetic facial traits: A genome-wide association study in the Chinese population.

Background: The aesthetic facial traits are closely related to life quality and strongly influenced by genetic factors, but the genetic predispositions in the Chinese population remain poorly understood. Methods: A genome-wide association studies (GWAS) and subsequent validations were performed in 26,806 Chinese on five facial traits: widow’s peak, unibrow, double eyelid, earlobe attachment, and freckles. Functional annotation was performed based on the expression quantitative trait loci (eQTL) variants, genome-wide polygenic scores (GPSs) were developed to represent the combined polygenic effects, and single nucleotide polymorphism (SNP) heritability was presented to evaluate the contributions of the variants. Results: In total, 21 genetic associations were identified, of which ten were novel: GMDS-AS1 (rs4959669, p = 1.29 × 10−49) and SPRED2 (rs13423753, p = 2.99 × 10−14) for widow’s peak, a previously unreported trait; FARSB (rs36015125, p = 1.96 × 10−21) for unibrow; KIF26B (rs7549180, p = 2.41 × 10−15), CASC2 (rs79852633, p = 4.78 × 10−11), RPGRIP1L (rs6499632, p = 9.15 × 10−11), and PAX1 (rs147581439, p = 3.07 × 10−8) for double eyelid; ZFHX3 (rs74030209, p = 9.77 × 10−14) and LINC01107 (rs10211400, p = 6.25 × 10−10) for earlobe attachment; and SPATA33 (rs35415928, p = 1.08 × 10−8) for freckles. Functionally, seven identified SNPs tag the missense variants and six may function as eQTLs. The combined polygenic effect of the associations was represented by GPSs and contributions of the variants were evaluated using SNP heritability. Conclusion: These identifications may facilitate a better understanding of the genetic basis of features in the Chinese population and hopefully inspire further genetic research on facial development.

Sex-Specific Genetic and Transcriptomic Liability to Neuroticism

BackgroundThe presentation, etiology, and relative risk of psychiatric disorders are strongly influenced by biological sex. Neuroticism is a transdiagnostic feature of psychiatric disorders displaying prominent sex differences. We performed genome-wide association studies of neuroticism separately in males and females to identify sex-specific genetic and transcriptomic profiles. MethodsNeuroticism scores were derived from the Eysenck Personality Inventory Neuroticism scale. Genome-wide association studies were performed in 145,669 females and 129,229 males from the UK Biobank considering autosomal and X chromosomal variation. Two-sided z tests were used to test for sex-specific effects of discovered loci, genetic correlates (n = 673 traits), tissue and gene transcriptomic profiles, and polygenic associations across health outcomes in the Vanderbilt University Biobank (39,692 females and 31,268 males). ResultsThe single nucleotide polymorphism heritability of neuroticism was not statistically different between males (h2 = 10.6%) and females (h2 = 11.85%). Four female-specific (rs10736549-CNTN5, rs6507056-ASXL3, rs2087182-MMS22L, and rs72995548-HSPB2) and 2 male-specific (rs10507274-MED13L and rs7984597) neuroticism risk loci reached genome-wide significance. Male- and female-specific neuroticism polygenic scores were most significantly associated with mood disorders (males: odds ratio = 1.11, p = 1.40 × 10−9; females: odds ratio = 1.14, p = 6.05 × 10−22). They also associated with sex-specific laboratory measurements related to erythrocyte count, distribution, and hemoglobin concentration. Gene expression variation in the pituitary was enriched for neuroticism loci in males (male: b = 0.026, p = .002), and genetically regulated transcriptomic changes highlighted the effect of SHISHA9, TEX26, and NCOA6. ConclusionsThrough a comprehensive assessment of genetic risk for neuroticism and the associated biological processes, this study identified several molecular pathways that can partially explain the known sex differences in neurotic symptoms and their psychiatric comorbidities.

Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms.

To investigate the genetic architecture of internalizing symptoms in childhood and adolescence. In 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument. The meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI= 0.84-2.48%, neffective= 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI= 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (|rg| > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range |rg|= 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Genetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

Comparing heritability estimators under alternative structures of linkage disequilibrium.

The single nucleotide polymorphism heritability of a trait is the proportion of its variance explained by the additive effects of the genome-wide single nucleotide polymorphisms. The existing approaches to estimate single nucleotide polymorphism heritability can be broadly classified into 2 categories. One set of approaches models the single nucleotide polymorphism effects as fixed effects and the other treats the single nucleotide polymorphism effects as random effects. These methods make certain assumptions about the dependency among individuals (familial relationship) as well as the dependency among markers (linkage disequilibrium) to provide consistent estimates of single nucleotide polymorphism heritability as the number of individuals increases. While various approaches have been proposed to account for such dependencies, it remains unclear which estimates reported in the literature are more robust against various model misspecifications. Here, we investigate the impact of different structures of linkage disequilibrium and familial relatedness on heritability estimation. We show that the performance of different methods for heritability estimation depends heavily on the structure of the underlying pattern of linkage disequilibrium and the degree of relatedness among sampled individuals. Moreover, we establish the equivalence between the 2 method-of-moments estimators, one using a fixed-single nucleotide polymorphism-effects approach, and another using a random-single nucleotide polymorphism-effects approach.

Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity.

Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency.