Accounting for ancestry: population substructure and genome-wide association studies

Historical and contemporary processes driving the origin and structure of an admixed population within a contact zone between subspecies of a north temperate diadromous fish

Hybridization between divergent lineages can result in losses of distinct evolutionary taxa. Alternatively, hybridization can lead to increased genetic variability that may fuel local adaptation and the generation of novel traits and/or taxa. Here, we examined single-nucleotide polymorphisms generated using genotyping-by-sequencing in a population of Dolly Varden char (Pisces: Salmonidae) that is highly admixed within a contact zone between two subspecies (Salvelinus malma malma, Northern Dolly Varden [NDV] and S. m. lordi, Southern Dolly Varden [SDV]) in southwestern Alaska to assess the spatial distribution of hybrids and to test hypotheses on the origin of the admixed population. Ancestry analysis revealed that this admixed population is composed of advanced generation hybrids between NDV and SDV or advanced backcrosses to SDV; no F1 hybrids were detected. Coalescent-based demographic modelling supported the origin of this population about 55,000 years ago by secondary contact between NDV and SDV with low levels of contemporary gene flow. Ancestry in NDV and SDV varies within the watershed and ancestry in NDV was positively associated with distance upstream from the sea, contingent on habitat-type sampled, and negatively associated with the number of migrations that individual fish made to the sea. Our results suggest that divergence between subspecies over hundreds of thousands of years may not be associated with significant reproductive isolation, but that elevated diversity owing to hybridization may have contributed to adaptive divergence in habitat use and life history.

Genomic Selection for Weaning Weight in Alpine Merino Sheep Based on GWAS Prior Marker Information

This study aims to compare the accuracy of genomic estimated breeding values (GEBV) estimated using a genomic best linear unbiased prediction (GBLUP) method and GEBV estimates incorporating prior marker information from a genome-wide association study (GWAS) for the weaning weight trait in highland Merino sheep. The objective is to provide theoretical and technical support for improving the accuracy of genomic selection. The study used a population of 1007 highland Merino ewes, with the weaning weight at 3 months as the target trait. The population was randomly divided into two groups. The first group was used for GWAS analysis to identify significant markers, and the top 5%, top 10%, top 15%, and top 20% markers were selected as prior marker information. The second group was used to estimate genetic parameters and compare the accuracy of GEBV predictions using different prior marker information. The accuracy was obtained using a five-fold cross-validation. Finally, both groups were subjected to cross-validation. The study's findings revealed that the heritability of the weaning weight trait, as calculated using the GBLUP model, ranged from 0.122 to 0.394, with corresponding prediction accuracies falling between 0.075 and 0.228. By incorporating prior marker information from GWAS, the heritability was enhanced to a range of 0.125 to 0.407. The inclusion of the top 5% to top 20% significant SNPs from GWAS results as prior information into GS showed potential for improving the accuracy of predicting genomic breeding value.

Rare Pathogenic Variants Identified in Whole Exome Sequencing of Monozygotic Twins With Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a childhood-onset complex neurodevelopmental disorder characterized by problems with communication and social interaction and restricted, repetitive, stereotyped behavior. The prevalence of ASD is one in 36 children. The genetic architecture of ASD is complex in spite of its high heritability. To identify the potential candidate genes of ASD, we carried out a comprehensive genetic study of monozygotic (MZ) twins concordant or discordant for ASD.

METHODS

Five MZ twins and their parents were recruited for the study. Four of the twins were concordant, whereas one was discordant for ASD. Whole exome sequencing was conducted for the twins and their parents. The exome DNA was enriched using Twist Human Customized Core Exome Kit, and paired-end sequencing was performed on HiSeq system.

RESULTS

We identified several rare and pathogenic variants (homozygous recessive, compound heterozygous, de novo) in ASD-affected individuals.

CONCLUSION

We report novel variants in individuals diagnosed with ASD. Several of these genes are involved in brain-related functions and not previously reported in ASD. Intriguingly, some of the variants were observed in the genes involved in sensory perception (auditory [MYO15A, PLEC, CDH23, UBR3, GPSM2], olfactory [OR9K2], gustatory [TAS2R31], and visual [CDH23, UBR3]). This is the first comprehensive genetic study of MZ twins in an Indian population. Further validation is required to determine whether these variants are associated with ASD.

Hepatic WDR23 proteostasis mediates insulin homeostasis by regulating insulin-degrading enzyme capacity

Maintaining insulin homeostasis is critical for cellular and organismal metabolism. In the liver, insulin is degraded by the activity of the insulin-degrading enzyme (IDE). Here, we establish a hepatic regulatory axis for IDE through WDR23-proteostasis. Wdr23KO mice have increased IDE expression, reduced circulating insulin, and defective insulin responses. Genetically engineered human cell models lacking WDR23 also increase IDE expression and display dysregulated phosphorylation of insulin signaling cascade proteins, IRS-1, AKT2, MAPK, FoxO, and mTOR, similar to cells treated with insulin, which can be mitigated by chemical inhibition of IDE. Mechanistically, the cytoprotective transcription factor NRF2, a direct target of WDR23-Cul4 proteostasis, mediates the enhanced transcriptional expression of IDE when WDR23 is ablated. Moreover, an analysis of human genetic variation in WDR23 across a large naturally aging human cohort in the US Health and Retirement Study reveals a significant association of WDR23 with altered hemoglobin A1C (HbA1c) levels in older adults, supporting the use of WDR23 as a new molecular determinant of metabolic health in humans.

Loci on chromosome 20 interact with rs16969968 to influence cigarettes per day in European ancestry individuals

BACKGROUND

The understanding of the molecular genetic contributions to smoking is largely limited to the additive effects of individual single nucleotide polymorphisms (SNPs), but the underlying genetic risk is likely to also include dominance, epistatic, and gene-environment interactions.

METHODS

To begin to address this complexity, we attempted to identify genetic interactions between rs16969968, the most replicated SNP associated with smoking quantity, and all SNPs and genes across the genome.

RESULTS

Using the UK Biobank European subsample, we found one SNP, rs1892967, and two genes, PCNA and TMEM230, that showed a significant genome-wide interaction with rs16969968 for log10 CPD and raw CPD, respectively, in a sample of 116 442 individuals who self-reported currently or previously smoking. We extended these analyses to individuals of South Asian descent and meta-analyzed the combined sample of 117 212 individuals of European and South Asian ancestry. We replicated the gene findings in a meta-analysis of five Finnish samples (N=40 140): FinHealth, FINRISK, Finnish Twin Cohort, GeneRISK, and Health-2000-2011.

CONCLUSIONS

To our knowledge, this represents the first reliable epistatic association between single nucleotide polymorphisms for smoking behaviors and provides a novel direction for possible future functional studies related to this interaction. Furthermore, this work demonstrates the feasibility of these analyses by pooling multiple datasets across various ancestries, which may be applied to other top SNPs for smoking and/or other phenotypes.

A naturally occurring variant of SHLP2 is a protective factor in Parkinson's disease

Mitochondrial DNA single nucleotide polymorphisms (mtSNPs) have been associated with a reduced risk of developing Parkinson's disease (PD), yet the underlying mechanisms remain elusive. In this study, we investigate the functional role of a PD-associated mtSNP that impacts the mitochondrial-derived peptide (MDP) Small Humanin-like Peptide 2 (SHLP2). We identify m.2158 T > C, a mtSNP associated with reduced PD risk, within the small open reading frame encoding SHLP2. This mtSNP results in an alternative form of SHLP2 (lysine 4 replaced with arginine; K4R). Using targeted mass spectrometry, we detect specific tryptic fragments of SHLP2 in neuronal cells and demonstrate its binding to mitochondrial complex 1. Notably, we observe that the K4R variant, associated with reduced PD risk, exhibits increased stability compared to WT SHLP2. Additionally, both WT and K4R SHLP2 show enhanced protection against mitochondrial dysfunction in in vitro experiments and confer protection against a PD-inducing toxin, a mitochondrial complex 1 inhibitor, in a mouse model. This study sheds light on the functional consequences of the m.2158 T > C mtSNP on SHLP2 and provides insights into the potential mechanisms by which this mtSNP may reduce the risk of PD.

A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders

Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.

Genome-Wide Association Analysis of Heat Tolerance in F2 Progeny from the Hybridization between Two Congeneric Oyster Species

As the world's largest farmed marine animal, oysters have enormous economic and ecological value. However, mass summer mortality caused by high temperature poses a significant threat to the oyster industry. To investigate the molecular mechanisms underlying heat adaptation and improve the heat tolerance ability in the oyster, we conducted genome-wide association analysis (GWAS) analysis on the F2 generation derived from the hybridization of relatively heat-tolerant Crassostrea angulata ♀ and heat-sensitive Crassostrea gigas ♂, which are the dominant cultured species in southern and northern China, respectively. Acute heat stress experiment (semi-lethal temperature 42 °C) demonstrated that the F2 population showed differentiation in heat tolerance, leading to extremely differentiated individuals (approximately 20% of individuals die within the first four days with 10% survival after 14 days). Genome resequencing and GWAS of the two divergent groups had identified 18 significant SNPs associated with heat tolerance, with 26 candidate genes located near these SNPs. Eleven candidate genes that may associate with the thermal resistance were identified, which were classified into five categories: temperature sensor (Trpm2), transcriptional factor (Gata3), protein ubiquitination (Ube2h, Usp50, Uchl3), heat shock subfamily (Dnajc17, Dnaja1), and transporters (Slc16a9, Slc16a14, Slc16a9, Slc16a2). The expressional differentiation of the above genes between C. gigas and C. angulata under sublethal temperature (37 °C) further supports their crucial role in coping with high temperature. Our results will contribute to understanding the molecular mechanisms underlying heat tolerance, and provide genetic markers for heat-resistance breeding in the oyster industry.

Sex and Race Differences in Obesity-Related Genetic Susceptibility and Risk of Cardiometabolic Disease in Older US Adults

Importance

The fat mass and obesity-associated gene (FTO) is associated with obesity phenotypes, but the association is inconsistent across populations. Within-population differences may explain some of the variability observed.

Objective

To investigate sex differences in the association between FTO single-nucleotide variants (SNVs) and obesity traits among self-identified non-Hispanic Black and non-Hispanic White US adults, to examine whether the SNVs were associated with cardiometabolic diseases, and to evaluate whether obesity mediated the association between FTO SNVs and cardiometabolic diseases.

Design, Setting, and Participants

This cross-sectional study used data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a US population-based cohort study with available genetic data (assayed in 2018) and phenotypic data at baseline (enrolled 2003-2007). Participants were aged 45 to 98 years at baseline. Data were analyzed from October 2021 to October 2022.

Exposures

Eleven SNVs in the FTO gene present among both Black and White participants.

Main Outcomes and Measures

Objectively measured obesity indicators (body mass index and waist-to-height ratio), objectively measured and/or self-reported cardiometabolic diseases (hypertension, stroke history, heart disease, and diabetes), and self-reported social-economic and psychosocial status.

Results

A total of 10 447 participants (mean [SD] age, 64.4 [9.7] years; 5276 [55.8%] women; 8743 [83.7%] Black and 1704 [16.3%] White) were included. In the White group, 11 FTO SNVs were significantly associated with obesity, hypertension, and diabetes using linear models (eg, body mass index: β = 0.536; 95% CI, 0.197-0.875), but none of the FTO SNVs were associated with obesity traits in the Black group. White males had a higher risk of obesity while White females had a higher risk of hypertension and diabetes. However, 1 FTO SNV (rs1121980) was associated with a direct increase in the risk of heart disease in Black participants not mediated by obesity (c' = 0.145 [SE, 0.0517]; P = .01).

Conclusions and Relevance

In this cross-sectional study of obesity phenotypes and their association with cardiometabolic diseases, the tested FTO SNVs reflected sex differences in White participants. Different patterns of associations were observed among self-identified Black participants. Therefore, these results could inform future work discovering risk alleles or risk scores unique to Black individuals or further investigating genetic risk in all US residents.

The Autosomal Short Tandem Repeat Polymorphisms Are Potentially Associated with Cardiovascular Disease Predisposition in the Latin American Population: A Mini Review

According to the World Health Organization, cardiovascular diseases (CVDs) are the leading cause of death worldwide across nearly all ethnic groups. Inherited cardiac conditions comprise a wide spectrum of diseases that affect the heart, including abnormal structural features and functional impairments. In Latin America, CVDs are the leading cause of death within the region. Factors such as population aging, unhealthy diet, obesity, smoking, and a sedentary lifestyle have increased the risk of CVD. The Latin American population is characterized by its diverse ethnic composition with varying percentages of each ancestral component (African, European, and Native American ancestry). Short tandem repeats (STRs) are DNA sequences with 2-6 base pair repetitions and constitute ~3% of the human genome. Importantly, significant allele frequency variations exist between different populations. While studies have described that STRs are in noncoding regions of the DNA, increasing evidence suggests that simple sequence repeat variations may be critical for proper gene activity and regulation. Furthermore, several STRs have been identified as potential disease predisposition markers. The present review is aimed at comparing and describing the frequencies of autosomal STR polymorphisms potentially associated with cardiovascular disease predisposition in Latin America compared with other populations.

Direct inference and control of genetic population structure from RNA sequencing data

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

Neurotransmitter Gene Polymorphisms Are Associated with Symptom Clusters in Patients Undergoing Radiation Therapy

OBJECTIVES

Purpose was to evaluate for associations between the severity of three distinct symptom clusters (ie, sickness-behavior, mood-cognitive, treatment-related) and polymorphisms for 16 genes involved in catecholaminergic, GABAergic, and serotonergic neurotransmission.

DATA SOURCES

Patients with breast and prostate cancer (n = 157) completed study questionnaires at the completion of radiation therapy. Memorial Symptom Assessment Scale was used to assess the severity of 32 common symptoms. Three distinct symptom clusters were identified using exploratory factor analysis. Associations between the symptom cluster severity scores and neurotransmitter gene polymorphisms were evaluated using regression analyses.

CONCLUSION

Severity scores for the sickness-behavior symptom cluster were associated with polymorphisms for solute carrier family 6 (SLC6A) member 2 (SLC6A2), SLC6A3, SLC6A1, and 5-hydroxytryptamine receptor (HTR) 2A (HTR2A) genes. For the mood-cognitive symptom cluster, severity scores were associated with polymorphisms for adrenoreceptor alpha 1D, SLC6A2, SLC6A3, SLC6A1, HTR2A, and HTR3A. Severity scores for the treatment-related symptom cluster were associated with polymorphisms for SLC6A2, SLC6A3, catechol-o-methyltransferase, SLC6A1, HTR2A, SLC6A4, and tryptophan hydroxylase 2.

IMPLICATIONS FOR NURSING PRACTICE

Findings suggest that polymorphisms for several neurotransmitter genes are involved in the severity of sickness-behavior, mood-cognitive, and treatment-related symptom clusters in oncology patients at the completion of radiation therapy. Four genes with various associated polymorphisms were common across the three distinct symptom clusters (ie, SLC6A2, SLC6A3, SLC6A1, HTR2A) which suggest that these clusters have common underlying mechanisms.

Impact of cross-ancestry genetic architecture on GWASs in admixed populations

Genome-wide association studies (GWASs) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWASs in admixed populations, such as the need to correctly adjust for population stratification. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing a GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes, we find that controlling for and conditioning effect sizes on local ancestry can reduce statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs, HetLanc is not large enough for GWASs to benefit from modeling heterogeneity in this way.

Study of Alzheimer's disease- and frontotemporal dementia-associated genes in the Cretan Aging Cohort

Using exome sequencing, we analyzed 196 participants of the Cretan Aging Cohort (CAC; 95 with Alzheimer's disease [AD], 20 with mild cognitive impairment [MCI], and 81 cognitively normal controls). The APOE ε4 allele was more common in AD patients (23.2%) than in controls (7.4%; p < 0.01) and the PSEN2 p.Arg29His and p.Cys391Arg variants were found in 3 AD and 1 MCI patient, respectively. Also, we found the frontotemporal dementia (FTD)-associated TARDBP gene p.Ile383Val variant in 2 elderly patients diagnosed with AD and in 2 patients, non CAC members, with the amyotrophic lateral sclerosis/FTD phenotype. Furthermore, the p.Ser498Ala variant in the positively selected GLUD2 gene was less frequent in AD patients (2.11%) than in controls (16%; p < 0.01), suggesting a possible protective effect. While the same trend was found in another local replication cohort (n = 406) and in section of the ADNI cohort (n = 808), this finding did not reach statistical significance and therefore it should be considered preliminary. Our results attest to the value of genetic testing to study aged adults with AD phenotype.

Identifying the genetic basis of viral spillover using Lassa virus as a test case

The rate at which zoonotic viruses spill over into the human population varies significantly over space and time. Remarkably, we do not yet know how much of this variation is attributable to genetic variation within viral populations. This gap in understanding arises because we lack methods of genetic analysis that can be easily applied to zoonotic viruses, where the number of available viral sequences is often limited, and opportunistic sampling introduces significant population stratification. Here, we explore the feasibility of using patterns of shared ancestry to correct for population stratification, enabling genome-wide association methods to identify genetic substitutions associated with spillover into the human population. Using a combination of phylogenetically structured simulations and Lassa virus sequences collected from humans and rodents in Sierra Leone, we demonstrate that existing methods do not fully correct for stratification, leading to elevated error rates. We also demonstrate, however, that the Type I error rate can be substantially reduced by confining the analysis to a less-stratified region of the phylogeny, even in an already-small dataset. Using this method, we detect two candidate single-nucleotide polymorphisms associated with spillover in the Lassa virus polymerase gene and provide generalized recommendations for the collection and analysis of zoonotic viruses.

Impact of cross-ancestry genetic architecture on GWAS in admixed populations

Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.

A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.

Genome-Wide Association Study of Adhesive Capsulitis Suggests Significant Genetic Risk Factors

BACKGROUND

Adhesive capsulitis of the shoulder involves loss of passive range of motion with associated pain and can develop spontaneously, with no obvious injury or inciting event. The pathomechanism of this disorder remains to be elucidated, but known risk factors for adhesive capsulitis include diabetes, female sex, and thyroid dysfunction. Additionally, transcriptional profiling and pedigree analyses have suggested a role for genetics. Identification of elements of genetic risk for adhesive capsulitis using population-based techniques can provide the basis for guiding both the personalized treatment of patients based on their genetic profiles and the development of new treatments by identification of the pathomechanism.

METHODS

A genome-wide association study (GWAS) was conducted using the U.K. Biobank (a collection of approximately 500,000 patients with genetic data and associated ICD-10 [International Classification of Diseases, 10th Revision] codes), comparing 2,142 patients with the ICD-10 code for adhesive capsulitis (M750) to those without. Separate GWASs were conducted controlling for 2 of the known risk factors of adhesive capsulitis-hypothyroidism and diabetes. Logistic regression analysis was conducted controlling for factors including sex, thyroid dysfunction, diabetes, shoulder dislocation, smoking, and genetics.

RESULTS

Three loci of significance were identified: rs34315830 (in WNT7B; odds ratio [OR] = 1.28; 95% confidence interval [CI], 1.22 to 1.39), rs2965196 (in MAU2; OR = 1.67; 95% CI, 1.39 to 2.00), and rs1912256 (in POU1F1; OR = 1.22; 95% CI, 1.14 to 1.31). These loci retained significance when controlling for thyroid dysfunction and diabetes. The OR for total genetic risk was 5.81 (95% CI, 4.08 to 8.31), compared with 1.70 (95% CI, 1.18 to 2.36) for hypothyroidism and 4.23 (95% CI, 2.32 to 7.05) for diabetes.

CONCLUSIONS

The total genetic risk associated with adhesive capsulitis was significant and similar to the risks associated with hypothyroidism and diabetes. Identification of WNT7B, POU1F1, and MAU2 implicates the Wnt pathway and cell proliferation response in the pathomechanism of adhesive capsulitis.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Estimating the causal effect of liability to disease on healthcare costs using Mendelian Randomization

Accurate measurement of the effects of disease status on healthcare costs is important in the pragmatic evaluation of interventions but is complicated by endogeneity bias. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We used a novel Mendelian Randomization analysis to model the causal impact on inpatient hospital costs of liability to six prevalent diseases and health conditions: asthma, eczema, migraine, coronary heart disease, Type 2 diabetes, and depression. We identified genetic variants from replicated genome-wide associations studies and estimated their association with inpatient hospital costs on over 300,000 individuals. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex and methods robust to violations of the exclusion restriction. Results overall were imprecise and we could not rule out large effects of liability to disease on healthcare costs. In particular, genetic liability to coronary heart disease had substantial impacts on costs.

TGFBR1*6A as a modifier of breast cancer risk and progression: advances and future prospects

There is growing evidence that germline mutations in certain genes influence cancer susceptibility, tumor evolution, as well as clinical outcomes. Identification of a disease-causing genetic variant enables testing and diagnosis of at-risk individuals. For breast cancer, several genes such as BRCA1, BRCA2, PALB2, ATM, and CHEK2 act as high- to moderate-penetrance cancer susceptibility genes. Genotyping of these genes informs genetic risk assessment and counseling, as well as treatment and management decisions in the case of high-penetrance genes. TGFBR1*6A (rs11466445) is a common variant of the TGF-β receptor type I (TGFBR1) that has a global minor allelic frequency (MAF) of 0.051 according to the 1000 Genomes Project Consortium. It is emerging as a high frequency, low penetrance tumor susceptibility allele associated with increased cancer risk among several cancer types. The TGFBR1*6A allele has been associated with increased breast cancer risk in women, OR 1.15 (95% CI 1.01–1.31). Functionally, TGFBR1*6A promotes breast cancer cell proliferation, migration, and invasion through the regulation of the ERK pathway and Rho-GTP activation. This review discusses current findings on the genetic, functional, and mechanistic associations between TGFBR1*6A and breast cancer risk and proposes future directions as it relates to genetic association studies and mechanisms of action for tumor growth, metastasis, and immune suppression.

Genetic Association between Different Metabolic Variants in APOA5 and PLIN1 in Type 2 Diabetes Mellitus among the Western Saudi Population: Case-Control Study

Type 2 diabetes mellitus (T2DM) is a multifactorial disease with a high global incidence. Hypertriglyceridemia is a major risk factor for both cardiovascular disease and T2DM. In this study, we determined the allele and genotype frequencies of apolipoprotein A5 (APOA5) single nucleotide polymorphism (SNP) rs662799 and perilipin 1 (PLIN1) SNPs rs894160, rs6496589, and rs1052700 and evaluated their association with T2DM risk in western Saudis. Only rs6496589 was found to be significantly associated with T2DM risk. We determined the risk allele for each SNP based on relative risk, and found that the G allele of rs662799, T allele of rs894160, G allele of r6496589, and T allele of rs1052700 correlated with T2DM risk. The effect of each SNP on T2DM risk and five of its clinical phenotypes was explored using multiple logistic regression. We found significant correlations between the C/G and G/G genotypes of rs6496589 and T2DM risk in the unadjusted model, whereas G/G was the only genotype that correlated with the risk of T2DM in the adjusted model. There was no significant correlation between rs662799, rs894160, and rs1052700 genotypes and T2DM risk. In conclusion, we have identified novel risk alleles and genotypes that contribute to genetic risk for T2DM in the western Saudi population.

Genetic variation in ALDH4A1 is associated with muscle health over the lifespan and across species

The influence of genetic variation on the aging process, including the incidence and severity of age-related diseases, is complex. Here, we define the evolutionarily conserved mitochondrial enzyme ALH-6/ALDH4A1 as a predictive biomarker for age-related changes in muscle health by combining Caenorhabditis elegans genetics and a gene-wide association scanning (GeneWAS) from older human participants of the US Health and Retirement Study (HRS). In a screen for mutations that activate oxidative stress responses, specifically in the muscle of C. elegans, we identified 96 independent genetic mutants harboring loss-of-function alleles of alh-6, exclusively. Each of these genetic mutations mapped to the ALH-6 polypeptide and led to the age-dependent loss of muscle health. Intriguingly, genetic variants in ALDH4A1 show associations with age-related muscle-related function in humans. Taken together, our work uncovers mitochondrial alh-6/ALDH4A1 as a critical component to impact normal muscle aging across species and a predictive biomarker for muscle health over the lifespan.

Ageing is inevitable, but what makes one person ‘age well’ and another decline more quickly remains largely unknown. While many aspects of ageing are clearly linked to genetics, the specific genes involved often remain unidentified.

Sarcopenia is an age-related condition affecting the muscles. It involves a gradual loss of muscle mass that becomes faster with age, and is associated with loss of mobility, decreased quality of life, and increased risk of death. Around half of all people aged 80 and over suffer from sarcopenia. Several lifestyle factors, especially poor diet and lack of exercise, are associated with the condition, but genetics is also involved: the condition accelerates more quickly in some people than others, and even fit, physically active individuals can be affected.

To study the genetics of conditions like sarcopenia, researchers often use animals like flies or worms, which have short generation times but share genetic similarities with humans. For example, the worm Caenorhabditis elegans has equivalents of several human muscle genes, including the gene alh-6. In worms, alh-6 is important for maintaining energy supply to the muscles, and mutating it not only leads to muscle damage but also to premature ageing. Given this insight, Villa, Stuhr, Yen et al. wanted to determine if variation in the human version of alh-6, ALDH4A1, also contributes to individual differences in muscle ageing and decline in humans.

Evaluating variation in this gene required a large amount of genetic data from older adults. These were taken from a continuous study that follows >35,000 older adults. Importantly, the study collects not only information on gene sequences but also measures of muscle health and performance over time for each individual. Analysis of these genetic data revealed specific small variations in the DNA of ALDH4A1, all of which associated with reduced muscle health.

Follow-up experiments in worms used genetic engineering techniques to test how variation in the worm alh-6 gene could influence age-related health. The resulting mutant worms developed muscle problems much earlier than their normal counterparts, supporting the role of alh-6/ALDH4A1 in determining muscle health across the lifespan of both worms and humans.

These results have identified a key influencer of muscle health during ageing in worms, and emphasize the importance of validating effects of genetic variation among humans during this process. Villa, Stuhr, Yen et al. hope that this study will help researchers find more genetic ‘markers’ of muscle health, and ultimately allow us to predict an individual’s risk of sarcopenia based on their genetic make-up.

Human genetic admixture through the lens of population genomics

Over the past 50 years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, ‘The Apportionment of Human Diversity’. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces ancestry-based structure into patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification of genetic targets of selection and prediction of complex traits. In this review, we outline some challenges for admixture population genetics, including limitations of applying methods designed for populations without recent admixture to the study of admixed populations. We highlight recent studies and methodological advances that aim to overcome such challenges, leveraging genomic signatures of admixture that occurred in the past tens of generations to gain insights into human history, natural selection and complex trait architecture.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

A deep learning framework for characterization of genotype data

Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality reduction to genotype data is known to capture genetic similarity between individuals, and is used for visualization of genetic variation, identification of population structure as well as ancestry mapping. Among frequently used methods are principal component analysis, which is a linear transform that often misses more fine-scale structures, and neighbor-graph based methods which focus on local relationships rather than large-scale patterns. Deep learning models are a type of nonlinear machine learning method in which the features used in data transformation are decided by the model in a data-driven manner, rather than by the researcher, and have been shown to present a promising alternative to traditional statistical methods for various applications in omics research. In this study, we propose a deep learning model based on a convolutional autoencoder architecture for dimensionality reduction of genotype data. Using a highly diverse cohort of human samples, we demonstrate that the model can identify population clusters and provide richer visual information in comparison to principal component analysis, while preserving global geometry to a higher extent than t-SNE and UMAP, yielding results that are comparable to an alternative deep learning approach based on variational autoencoders. We also discuss the use of the methodology for more general characterization of genotype data, showing that it preserves spatial properties in the form of decay of linkage disequilibrium with distance along the genome and demonstrating its use as a genetic clustering method, comparing results to the ADMIXTURE software frequently used in population genetic studies.

The influence of host genetics on liver microbiome composition in patients with NAFLD

Background

Human body microbiotas are influenced by several factors, including the interaction of the host with the environment and dietary preferences. The role of host genetics in modulating the liver microbiota in the context of NAFLD remains unknown. To address this gap, we examined the interplay between the liver metataxonomic profile and host genetics.

Methods

We obtained 16S rRNA gene sequences from liver biopsies and genotypes by Taqman-assays in 116 individuals. We compared taxon abundance at the genus level across host genotypes using dominant models of inheritance. We focused the analysis on variants influencing the risk/ protection against NAFLD-histological severity (PNPLA3-rs738409, TM6SF2-rs58542926, MBOAT7-rs641738, and HSD17B13-rs72613567) and a variant influencing macronutrient intake (FGF21-rs838133). We also explored the variants' combined effect via a polygenic risk score (PRS).

Findings

We identified at least 18 bacterial taxa associated with variants in the selected loci. Members of the Gammaproteobacteria class were significantly enriched in carriers of the rs738409 and rs58542926 risk-alleles, including Enterobacter (fold change [FC]=6.2) and Pseudoalteromonas (FC=2) genera, respectively. Lawsonella (1.6-FC), Prevotella_9 (FC=1.5), and Staphylococcus (FC=1.3) genera were enriched in rs838133-minor allele carriers, which is linked to sugar consumption and carbohydrate intake. Tyzzerella abundance (FC=2.64) exhibited the strongest association (p = 0.0019) with high PRS values (>4 risk alleles). The percentage of genus-level taxa variation explained by the PRS was ∼7.4%, independently of liver steatosis score and obesity.

Interpretation

We provided evidence that genetic variation may influence the liver microbial DNA composition. These observations may represent potentially actionable mechanisms of disease.

Adiponectin gene polymorphisms and posttraumatic stress disorder symptoms among female rape survivors: an exploratory study

Background: Rape is a common traumatic event which may result in the development of posttraumatic stress disorder (PTSD), yet few studies have investigated risk biomarkers in sexually traumatised individuals. Adiponectin is a novel cytokine within inflammatory and cardiometabolic pathways with evidence of involvement in PTSD. Objective: This prospective exploratory study in a sample of female rape survivors investigated the association of single nucleotide polymorphisms (SNPs) in the adiponectin gene (ADIPOQ) and posttraumatic stress symptom (PTSS) severity, and the interaction of these SNPs of interest with childhood trauma in modifying the association with PTSS severity. Method: The study involved 455 rape-exposed black South African women (mean age (SD), 25.3 years (±5.5)) recruited within 20 days of being raped. PTSS was assessed using the Davidson Trauma Scale (DTS) and childhood trauma was assessed using a modified version of the Childhood Trauma Scale-Short Form Questionnaire. Eight ADIPOQ SNPs (rs17300539, rs16861194, rs16861205, rs2241766, rs6444174, rs822395, rs1501299, rs1403697) were genotyped using KASP. Mixed linear regression models were used to test additive associations of ADIPOQ SNPs and PTSS severity at baseline, 3 and 6 months following rape. Results: The mean DTS score post-sexual assault was high (71.3 ± 31.5), with a decrease in PTSS severity shown over time for all genotypes. rs6444174TT genotype was inversely associated with baseline PTSS in the unadjusted model (β = -13.6, 95% CI [-25.1; -2.1], p = .021). However, no genotype was shown to be significantly associated with change in PTSS severity over time and therefore ADIPOQ SNP x childhood trauma interaction was not further investigated. Conclusion: None of the ADIPOQ SNPs selected for investigation in this population were shown to be associated with change in PTSS severity over a 6-month period and therefore their clinical utility as risk biomarkers for rape-related PTSD appears limited. These SNPs should be further investigated in possible gene-gene and gene-environment interactions.

An Overview of Strategies for Detecting Genotype-Phenotype Associations Across Ancestrally Diverse Populations

Genome-wide association studies (GWAS) have been very successful at identifying genetic variants influencing a large number of traits. Although the great majority of these studies have been performed in European-descent individuals, it has been recognised that including populations with differing ancestries enhances the potential for identifying causal SNPs due to their differing patterns of linkage disequilibrium. However, when individuals from distinct ethnicities are included in a GWAS, it is necessary to implement a number of control steps to ensure that the identified associations are real genotype-phenotype relationships. In this Review, we discuss the analyses that are required when performing multi-ethnic studies, including methods for determining ancestry at the global and local level for sample exclusion, controlling for ancestry in association testing, and post-GWAS interrogation methods such as genomic control and meta-analysis. We hope that this overview provides a primer for those researchers interested in including distinct populations in their studies.

Boosting the potential of cattle breeding using molecular biology, genetics, and bioinformatics approaches – a review

Cattle are among the most important farm animals that underwent an intense selection with the aim to increase milk production and to improve growth and meat properties, meanwhile reducing the generation interval allowing for a faster herd turnover. Recently, a shift from traditional breeding methods to breeding based on genetic testing has been observed. In this perspective, we review the techniques of molecular biology, genetics, and bioinformatics that are expected to further boost the agricultural potential of cattle. We discuss embryo selection based on next-generation and Nanopore sequencing and in vitro embryo production, boosting the potential of genetically superior animals. Gene editing of embryos could further speed up the selection process, essentially introducing a change in a single generation. Lastly, we discuss the host-microbiome co-evolution and adaptation. For example, cattle already adapted to low-quality low-cost fodder could be bred to achieve desired properties for the beef and dairy industry. The challenge of breeding and genetic editing is to accompany the selection on desired consumer-oriented traits with the push for sustainability and the adaptation to a changing climate while remaining economically viable. We propose that we are yet to see the limits of what is possible to achieve with modern technology for the cattle of the future; the ultimate goal will be to produce and maintain genetically elite individuals that can sustain the growing demands on the production.

Challenges and Opportunities in Understanding Genetics of Fungal Diseases: Towards a Functional Genomics Approach

Infectious diseases are a leading cause of morbidity and mortality worldwide, and human pathogens have long been recognized as one of the main sources of evolutionary pressure, resulting in a high variable genetic background in immune-related genes. The study of the genetic contribution to infectious diseases has undergone tremendous advances over the last decades. Here, focusing on genetic predisposition to fungal diseases, we provide an overview of the available approaches for studying human genetic susceptibility to infections, reviewing current methodological and practical limitations. We describe how the classical methods available, such as family-based studies and candidate gene studies, have contributed to the discovery of crucial susceptibility factors for fungal infections. We will also discuss the contribution of novel unbiased approaches to the field, highlighting their success but also their limitations for the fungal immunology field. Finally, we show how a systems genomics approach can overcome those limitations and can lead to efficient prioritization and identification of genes and pathways with a critical role in susceptibility to fungal diseases. This knowledge will help to stratify at-risk patient groups and, subsequently, develop early appropriate prophylactic and treatment strategies.

Population-specific genetic background for the OPRM1 variant rs1799971 (118A>G): implications for genomic medicine and functional analysis

The mu-opioid receptor (MOR, OPRM1) has important roles in diverse functions including reward, addiction, and response to pain treatment. SNP rs1799971 (118A > G, N40D) which occur at a high frequency (40-60%) in Asia and moderate frequency (15%) in samples of European ancestry, is the only common coding variant in the canonical transcript, in non-African populations. Despite extensive studies, the molecular consequences of this variation remained unresolved. The aim of this study was to determine the genetic background of the OPRM1 region of 118G in four representative populations and to assess its potential modulatory effect. Seven common haplotypes with distinct population distribution were identified based on seven SNPs. Three haplotypes carry the 118G and additional highly linked regulatory SNPs (e.g., rs9383689) that could modulate the effect of 118G. Extended analysis in the 1000 Genomes database (n = 2504) revealed a common East Asian-specific haplotype with a different genetic background in which there are no variant alleles for an upstream LD block tagged by the eQTL rs9397171. The major European haplotype specifically includes the eQTL intronic SNP rs62436463 that must have arisen after the split between European and Asian populations. Differentiating between the effect of 118G and these SNPs requires specific experimental approaches. The analysis also revealed a significant increase in two 118A haplotypes with eQTL SNPs associated with drug addiction (rs510769) and obesity (rs9478496) in populations with native Mexican ancestry. Future studies are required to assess the clinical implication of these findings.

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures

The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpretability and biological replication. It also reduces potentially confounding effects of population substructure on outcome prediction. The TM consists of three main components. First, replicable but flexible feature engineering procedures translate genome-scale data into biologically informative features that appropriately contextualize simple variant calls/genotypes within biological and functional contexts. Second, model-free, nonparametric ML-based feature filtering procedures empirically reduce dimensionality and noise of both original genotype calls and engineered features. Third, a powerful ML algorithm for feature selection is used to differentiate risk variant contributions across variant frequency and functional prediction spectra. The TM simultaneously evaluates potential contributions of variants operative under polygenic and heterogeneous models of genetic architecture. Our TM enables integration of biological information (e.g., genomic annotations) within conceptual frameworks akin to geneset-/pathways-based and collapsing methods, but overcomes some of these methods' limitations. The full TM pipeline is executed in R. Our approach and initial findings from its application to a whole-exome schizophrenia case-control data set are presented. These TM procedures extend the findings of the primary investigation and yield novel results.

Ancestral genetic legacy of the extant population of Argentina as predicted by autosomal and X-chromosomal DIPs

Aiming to determine their ancestry diagnostic potential, we selected two sets of nuclear deletion/insertion polymorphisms (DIPs), including 30 located on autosomal chromosomes and 33 on the X chromosome. We analysed over 200 unrelated Argentinean individuals living in urban areas of Argentina. As in most American countries, the extant Argentinean population is the result of tricontinental genetic admixture. The peopling process within the continent was characterised by mating bias involving Native American and enslaved African females and European males. Differential results were detected between autosomal DIPs and X-DIPs. The former showed that the European component was the largest (77.8%), followed by the Native American (17.9%) and African (4.2%) components, in good agreement with the previously published results. In contrast, X-DIPs showed that the European genetic contribution was also predominant but much smaller (52.9%) and considerably larger Native American and African contributions (39.6% and 7.5%, respectively). Genetic analysis revealed continental genetic contributions whose associated phenotypic traits have been mostly lost. The observed differences between the estimated continental genetic contribution proportions based on autosomal DIPs and X-DIPs reflect the effects of autosome and X-chromosome transmission behaviour and their different recombination patterns. This work shows the ability of the tested DIP panels to infer ancestry and confirm mating bias. To the best of our knowledge, this is the first study focusing on ancestry-informative autosomal DIP and X-DIP comparisons performed in a sample representing the entire Argentinean population.

Genetic ancestry plays a central role in population pharmacogenomics

Recent studies have pointed out the essential role of genetic ancestry in population pharmacogenetics. In this study, we analyzed the whole-genome sequencing data from The 1000 Genomes Project (Phase 3) and the pharmacogenetic information from Drug Bank, PharmGKB, PharmaADME, and Biotransformation. Here we show that ancestry-informative markers are enriched in pharmacogenetic loci, suggesting that trans-ancestry differentiation must be carefully considered in population pharmacogenetics studies. Ancestry-informative pharmacogenetic loci are located in both protein-coding and non-protein-coding regions, illustrating that a whole-genome analysis is necessary for an unbiased examination over pharmacogenetic loci. Finally, those ancestry-informative pharmacogenetic loci that target multiple drugs are often a functional variant, which reflects their importance in biological functions and pathways. In summary, we develop an efficient algorithm for an ultrahigh-dimensional principal component analysis. We create genetic catalogs of ancestry-informative markers and genes. We explore pharmacogenetic patterns and establish a high-accuracy prediction panel of genetic ancestry. Moreover, we construct a genetic ancestry pharmacogenomic database Genetic Ancestry PhD (http://hcyang.stat.sinica.edu.tw/databases/genetic_ancestry_phd/).

Hsin-Chou Yang et al. examine population structure in several genomic databases and identify that pharmacogenetic loci are enriched for markers of genetic ancestry. Their results suggest that genetic ancestry must be carefully considered in population pharmacogenetics studies.

Identification of ancestry proportions in admixed groups across the Americas using clinical pharmacogenomic SNP panels

We evaluated the performance of three PGx panels to estimate biogeographical ancestry: the DMET panel, and the VIP and Preemptive PGx panels described in the literature. Our analysis indicate that the three panels capture quite well the individual variation in admixture proportions observed in recently admixed populations throughout the Americas, with the Preemptive PGx and DMET panels performing better than the VIP panel. We show that these panels provide reliable information about biogeographic ancestry and can be used to guide the implementation of PGx clinical decision-support (CDS) tools. We also report that using these panels it is possible to control for the effects of population stratification in association studies in recently admixed populations, as exemplified with a warfarin dosing GWA study in a sample from Brazil.

Explore the genetics of weedy traits using rice 3K database

BACKGROUND

Weedy rice, a conspecific weedy counterpart of the cultivated rice (Oryza sativa L.), has been problematic in rice-production area worldwide. Although we started to know about the origin of some weedy traits for some rice-growing regions, an overall assessment of weedy trait-related loci was not yet available. On the other hand, the advances in sequencing technologies, together with community efforts, have made publicly available a large amount of genomic data. Given the availability of public data and the need of "weedy" allele mining for a better management of weedy rice, the objective of the present study was to explore the genetic architecture of weedy traits based on publicly available data, mainly from the 3000 Rice Genome Project (3K-RGP).

RESULTS

Based on the results of population structure analysis, we have selected 1378 individuals from four sub-populations (aus, indica, temperate japonica, tropical japonica) without admixed genomic composition for genome-wide association analysis (GWAS). Five traits were investigated: awn color, seed shattering, seed threshability, seed coat color, and seedling height. GWAS was conducted for each sub-population × trait combination and we have identified 66 population-specific trait-associated SNPs. Eleven significant SNPs fell into an annotated gene and four other SNPs were close to a putative candidate gene (± 25 kb). SNPs located in or close to Rc were particularly predictive of the occurrence of seed coat color and our results showed that different sub-populations required different SNPs for a better seed coat color prediction. We compared the data of 3K-RGP to a publicly available weedy rice dataset. The profile of allele frequency, phenotype-genotype segregation of target SNP, as well as GWAS results for the presence and absence of awns diverged between the two sets of data.

CONCLUSIONS

The genotype of trait-associated SNPs identified in this study, especially those located in or close to Rc, can be developed to diagnostic SNPs to trace the origin of weedy trait occurred in the field. The difference of results from the two publicly available datasets used in this study emphasized the importance of laboratory experiments to confirm the allele mining results based on publicly available data.

Probably Correct: Rescuing Repeats with Short and Long Reads

Ever since the introduction of high-throughput sequencing following the human genome project, assembling short reads into a reference of sufficient quality posed a significant problem as a large portion of the human genome-estimated 50-69%-is repetitive. As a result, a sizable proportion of sequencing reads is multi-mapping, i.e., without a unique placement in the genome. The two key parameters for whether or not a read is multi-mapping are the read length and genome complexity. Long reads are now able to span difficult, heterochromatic regions, including full centromeres, and characterize chromosomes from "telomere to telomere". Moreover, identical reads or repeat arrays can be differentiated based on their epigenetic marks, such as methylation patterns, aiding in the assembly process. This is despite the fact that long reads still contain a modest percentage of sequencing errors, disorienting the aligners and assemblers both in accuracy and speed. Here, I review the proposed and implemented solutions to the repeat resolution and the multi-mapping read problem, as well as the downstream consequences of reference choice, repeat masking, and proper representation of sex chromosomes. I also consider the forthcoming challenges and solutions with regards to long reads, where we expect the shift from the problem of repeat localization within a single individual to the problem of repeat positioning within pangenomes.

Mapping the genetic basis of diabetes mellitus in the Australian Burmese cat (Felis catus)

Diabetes mellitus, a common endocrinopathy affecting domestic cats, shares many clinical and pathologic features with type 2 diabetes in humans. In Australia and Europe, diabetes mellitus is almost four times more common among Burmese cats than in other breeds. As a genetically isolated population, the diabetic Australian Burmese cat provides a spontaneous genetic model for studying diabetes mellitus in humans. Studying complex diseases in pedigreed breeds facilitates tighter control of confounding factors including population stratification, allelic frequencies and environmental heterogeneity. We used the feline SNV array and whole genome sequence data to undertake a genome wide-association study and runs of homozygosity analysis, of a case–control cohort of Australian and European Burmese cats. Our results identified diabetes-associated haplotypes across chromosomes A3, B1 and E1 and selective sweeps across the Burmese breed on chromosomes B1, B3, D1 and D4. The locus on chromosome B1, common to both analyses, revealed coding and splice region variants in candidate genes, ANK1, EPHX2 and LOX2, implicated in diabetes mellitus and lipid dysregulation. Mapping this condition in Burmese cats has revealed a polygenic spectrum, implicating loci linked to pancreatic beta cell dysfunction, lipid dysregulation and insulin resistance in the pathogenesis of diabetes mellitus in the Burmese cat.

Sex-specific pathways among tri-allelic serotonin transporter polymorphism, trait neuroticism and generalized anxiety disorder

BACKGROUND

Neuroticism personality trait is recognized as an important endophenotypic predictor of generalized anxiety disorder (GAD). Furthermore, endophenotype-based pathway approaches have recently been shown to have greater advantages for gene-finding strategies than traditional case-control studies. In the present study, in addition to conventional case-control methods, we used pathway analyses to test whether the tri-allelic serotonin transporter promoter polymorphism (combining 5-HTTLPR and rs25531) is associated with risk of GAD through its effects on trait neuroticism.

METHODS

We included 2236 Han Chinese adults in this study, including 736 patients with GAD and 1500 healthy participants. We genotyped the 5-HTTLPR and rs25531 polymorphisms using the polymerase chain reaction restriction fragment length polymorphism method. We used the Neuroticism scale of the Maudsley Personality Inventory (MPI) short version (MPI-Neuroticism) to measure participants' tendency toward neuroticism.

RESULTS

Using endophenotype-based path analyses, we found significant indirect effects of the tri-allelic genotype on risk of GAD, mediated by MPI-Neuroticism in both men and women. Compared to women carrying the S'S' genotype, women carrying the L' allele had higher levels of MPI-Neuroticism, which in turn were associated with higher risk of GAD. Men, however, showed the opposite pattern. Using traditional case-control comparisons, we observed that the effect of tri-allelic genotype on GAD was significant, but only in women.

LIMITATIONS

Participants were restricted to Han Chinese, and we used only 1 questionnaire to assess neuroticism.

CONCLUSION

These findings are the first to show that the triallelic 5-HTTLPR polymorphism is associated with elevated risk of GAD, and that this effect is mediated via increased trait neuroticism, a sex-dependent risk pathway.

MYBPC3 Haplotype Linked to Hypertrophic Cardiomyopathy in Rhesus Macaques (Macaca mulatta)

In humans, abnormal thickening of the left ventricle of the heart clinically defines hypertrophic cardiomyopathy (HCM), a common inherited cardiovascular disorder that can precede a sudden cardiac death event. The wide range of clinical presentations in HCM obscures genetic variants that may influence an individual's susceptibility to sudden cardiac death. Although exon sequencing of major sarcomere genes can be used to detect high-impact causal mutations, this strategy is successful in only half of patient cases. The incidence of left ventricular hypertrophy (LVH) in a managed research colony of rhesus macaques provides an excellent comparative model in which to explore the genomic etiology of severe HCM and sudden cardiac death. Because no rhesus HCM-associated mutations have been reported, we used a next-generation genotyping assay that targets 7 sarcomeric rhesus genes within 63 genomic sites that are orthologous to human genomic regions known to harbor HCM disease variants. Amplicon sequencing was performed on 52 macaques with confirmed LVH and 42 unrelated, unaffected animals representing both the Indian and Chinese rhesus macaque subspecies. Bias-reduced logistic regression uncovered a risk haplotype in the rhesus MYBPC3 gene, which is frequently disrupted in both human and feline HCM; this haplotype implicates an intronic variant strongly associated with disease in either homozygous or carrier form. Our results highlight that leveraging evolutionary genomic data provides a unique, practical strategy for minimizing population bias in complex disease studies.

Genetic Structural Differentiation Analyses of Intercontinental Populations and Ancestry Inference of the Chinese Hui Group Based on a Novel Developed Autosomal AIM-InDel Genotyping System

In the present study, we investigated the genetic polymorphisms of 39 ancestry informative marker-insertion/deletion (AIM-InDel) loci in the Chinese Hui group using a previously self-developed panel, further clarified the genetic relationships between the Hui group and other reference populations, and assessed the ancestry inference efficiency of the AIM-InDel panel based on the worldwide population data from 1000 Genomes Phase 3. The results of the locus-specific informativeness (I_n) and pairwise fixation index (F_st) values, multidimensional scaling analysis, and success ratio of estimation with cross-validation showed that the novel panel could well reveal the genetic structural differentiations of the East Asian, European, African, and South Asian populations. Besides, the biogeographical ancestry origin inference both at the individual and population levels was conducted on the Chinese Hui group by principal component analysis and STRUCTURE analysis, and the results revealed that the Hui group had the East Asian origin, and the East Asian component ratio of Hui group was approximately 88.87%. Furthermore, the population genetic analyses among the Hui group and reference populations were performed based on the insertion allele frequency heat map, population pairwise F_st values and phylogenetic tree, and the results indicated that the Hui group was genetically closer to East Asian populations, especially two Chinese Han populations (CHS and CHB populations).

Sleep disruption and duration are associated with variants in genes involved in energy homeostasis in adults with HIV/AIDS

OBJECTIVE

To determine whether selected genes and plasma markers involved in energy homeostasis are associated with sleep disruption or duration in adults with HIV/AIDS.

METHODS

A sample of 289 adults with HIV/AIDS wore a wrist actigraph for 72 h to estimate total sleep time (TST) and wake after sleep onset (WASO). Twenty-three single nucleotide polymorphisms (SNP) spanning 5 energy homeostasis genes (adiponectin [ADIPOQ], ghrelin [GHRL], leptin [LEP], peroxisome proliferator-activated receptor-alpha [PPARA], and -gamma [PPARG]) were genotyped using a custom array. Plasma markers of energy homeostasis (adiponectin, ghrelin, leptin) were measured by commercial multiplex assay.

RESULTS

After adjusting for demographic and clinical characteristics (race/ethnicity, gender, CD4 cell count, waist circumference, medications), both WASO and TST were associated with SNPs in ADIPOQ (rs182052), LEP (rs10244329, rs3828942), PPARA (rs135551, rs4253655), and PPARG (rs709151). Additional SNPs in ADIPOQ were associated with WASO (rs1501299, rs3821799, rs6773957) and TST (rs2241766). TST was also associated with SNPs in GHRL (rs26802), LEP (rs11760956), PPARA (rs135547, rs8138102, rs4253776), and PPARG (rs12490265, rs796313). Many covariate-adjusted associations involved a significant interaction with markers of HIV (viral load, years since diagnosis). Among plasma markers, higher adiponectin was associated with less WASO, higher ghrelin and glucose levels with shorter TST, and higher leptin with longer TST.

CONCLUSIONS

Replication of SNPs in all five genes and three plasma markers of energy homeostasis were associated with objective sleep measures. HIV disease influenced many of the associations. Findings strengthen evidence for associations between energy homeostasis genetics and poor sleep, and provide direction for pharmacological intervention research.

Evaluation of the Precision of Ancestry Inferences in South American Admixed Populations

Ancestry informative markers (AIMs) are used in forensic genetics to infer biogeographical ancestry (BGA) of individuals and may also have a prominent role in future police and identification investigations. In the last few years, many studies have been published reporting new AIM sets. These sets include markers (usually around 100 or less) selected with different purposes and different population resolutions. Regardless of the ability of these sets to separate populations from different continents or regions, the uncertainty associated with the estimates provided by these panels and their capacity to accurately report the different ancestral contributions in individuals of admixed populations has rarely been investigated. This issue is addressed in this study by evaluating different AIM sets. Ancestry inference was carried out in admixed South American populations, both at population and individual levels. The results of ancestry inferences using AIM sets with different numbers of markers among admixed reference populations were compared. To evaluate the performance of the different ancestry panels at the individual level, expected and observed estimates among families and their offspring were compared, considering that (1) the apportionment of ancestry in the offspring should be closer to the average ancestry of the parents, and (2) full siblings should present similar ancestry values. The results obtained illustrate the importance of having a good balance/compromise between not only the number of markers and their ability to differentiate ancestral populations, but also a balanced differentiation among reference groups, to obtain more precise values of genetic ancestry. This work also highlights the importance of estimating errors associated with the use of a limited number of markers. We demonstrate that although these errors have a moderate effect at the population level, they may have an important impact at the individual level. Considering that many AIM-sets are being described for inferences at the individual level and not at the population level, e.g., in association studies or the determination of a suspect's BGA, the results of this work point to the need of a more careful evaluation of the uncertainty associated with the ancestry estimates in admixed populations, when small AIM-sets are used.

Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize

Crops are hosts to numerous plant pathogenic microorganisms. Maize has several major disease issues; thus, breeding multiple disease resistant (MDR) varieties is critical. While the genetic basis of resistance to multiple fungal pathogens has been studied in maize, less is known about the relationship between fungal and bacterial resistance. In this study, we evaluated a disease resistance introgression line (DRIL) population for the foliar disease Goss’s bacterial wilt and blight (GW) and conducted quantitative trait locus (QTL) mapping. We identified a total of ten QTL across multiple environments. We then combined our GW data with data on four additional foliar diseases (northern corn leaf blight, southern corn leaf blight, gray leaf spot, and bacterial leaf streak) and conducted multivariate analysis to identify regions conferring resistance to multiple diseases. We identified 20 chromosomal bins with putative multiple disease effects. We examined the five chromosomal regions (bins 1.05, 3.04, 4.06, 8.03, and 9.02) with the strongest statistical support. By examining how each haplotype effected each disease, we identified several regions associated with increased resistance to multiple diseases and three regions associated with opposite effects for bacterial and fungal diseases. In summary, we identified several promising candidate regions for multiple disease resistance in maize and specific DRILs to expedite interrogation.

Genetic Survey of Adult-Onset Idiopathic Intracranial Hypertension

BACKGROUND

Idiopathic intracranial hypertension (IIH) is a condition characterized by increased intracranial pressure of unknown cause. IIH has been shown to be associated with female sex as well as obesity. This genome-wide association study was performed to determine whether genetic variants are associated with this condition.

METHODS

We analyzed the chromosomal DNA of 95 patients with IIH enrolled in the Idiopathic Intracranial Hypertension Treatment Trial and 95 controls matched on sex, body mass index, and self-reported ethnicity. The samples were genotyped using Illumina Infinium HumanCoreExome v1-0 array and analyzed using a generalized linear mixed model that accounted for population stratification using multidimensional scaling.

RESULTS

A total of 301,908 single nucleotide polymorphisms (SNPs) were evaluated. The strongest associations observed were for rs2234671 on chromosome 2 (P = 4.93 × 10), rs79642714 on chromosome 6 (P = 2.12 × 10), and rs200288366 on chromosome 12 (P = 6.23 × 10). In addition, 3 candidate regions marked by multiple associated SNPs were identified on chromosome 5, 13, and 14.

CONCLUSIONS

This is the first study to investigate the genetics of IIH in a rigorously characterized cohort. The study was limited by its modest size and thus would have only been able to demonstrate highly significant association on a genome-wide scale for relatively common alleles exerting large effects. However, several variants and loci were identified that might be strong candidates for follow-up studies in other well-phenotyped cohorts.

On rare variants in principal component analysis of population stratification

Background

Population stratification is a known confounder of genome-wide association studies, as it can lead to false positive results. Principal component analysis (PCA) method is widely applied in the analysis of population structure with common variants. However, it is still unclear about the analysis performance when rare variants are used.

Results

We derive a mathematical expectation of the genetic relationship matrix. Variance and covariance elements of the expected matrix depend explicitly on allele frequencies of the genetic markers used in the PCA analysis. We show that inter-population variance is solely contained in K principal components (PCs) and mostly in the largest K-1 PCs, where K is the number of populations in the samples. We propose F_PC, ratio of the inter-population variance to the intra-population variance in the K population informative PCs, and d², sum of squared distances among populations, as measures of population divergence. We show analytically that when allele frequencies become small, the ratio F_PC abates, the population distance d² decreases, and portion of variance explained by the K PCs diminishes. The results are validated in the analysis of the 1000 Genomes Project data. The ratio F_PC is 93.85, population distance d² is 444.38, and variance explained by the largest five PCs is 17.09% when using with common variants with allele frequencies between 0.4 and 0.5. However, the ratio, distance and percentage decrease to 1.83, 17.83 and 0.74%, respectively, with rare variants of frequencies between 0.0001 and 0.01.

Conclusions

The PCA of population stratification performs worse with rare variants than with common ones. It is necessary to restrict the selection to only the common variants when analyzing population stratification with sequencing data.

The Effect Sizes of PPARγ rs1801282, FTO rs9939609, and MC4R rs2229616 Variants on Type 2 Diabetes Mellitus Risk among the Western Saudi Population: A Cross-Sectional Prospective Study

Type 2 diabetes mellitus (T2DM) is a common polygenic disease with associated comorbidities. Obesity is a major risk factor for the development of T2DM. The aim of this study is to determine the allele and genotype frequency of peroxisome proliferator-activated receptor-γ (PPARγ) rs1801282, fat mass and obesity-associated protein (FTO) rs9939609, and melanocortin 4 receptor (MC4R) rs2229616 polymorphisms and their association with risk of T2DM in the western Saudi population as mediators of adiposity phenotypes. In a cross-sectional prospective study, genomic DNA from control and T2DM patients were isolated and genotyped for these single-nucleotide polymorphisms. There was a significant association of the MC4R rs2229616 variant with T2DM, but no association with T2DM was detected with PPARγ rs1801282 or FTO rs9939609. The combination of C/C for PPARγ rs1801282, A/A for FTO rs9939609, and C/C for MC4R rs2229616 increased the risk of T2DM by 1.82. The A/T genotype for FTO rs9939609 was predicted to decrease the risk of T2DM when combined with C/C for PPARγ rs1801282 and C/C for MC4R rs2229616 or C/C for PPARγ rs1801282 and C/T MC4R rs2229616. In conclusion, our study showed the risk of the assessed variants for the development of T2DM in the Saudi population.

Genome-Wide Analyses and Prediction of Resistance to MLN in Large Tropical Maize Germplasm

Maize lethal necrosis (MLN), caused by co-infection of maize chlorotic mottle virus and sugarcane mosaic virus, can lead up to 100% yield loss. Identification and validation of genomic regions can facilitate marker assisted breeding for resistance to MLN. Our objectives were to identify marker-trait associations using genome wide association study and assess the potential of genomic prediction for MLN resistance in a large panel of diverse maize lines. A set of 1400 diverse maize tropical inbred lines were evaluated for their response to MLN under artificial inoculation by measuring disease severity or incidence and area under disease progress curve (AUDPC). All lines were genotyped with genotyping by sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability estimates were moderate to high. GWAS revealed 32 significantly associated SNPs for MLN resistance (at p < 1.0 × 10⁻⁶). For disease severity, these significantly associated SNPs individually explained 3–5% of the total phenotypic variance, whereas for AUDPC they explained 3–12% of the total proportion of phenotypic variance. Most of significant SNPs were consistent with the previous studies and assists to validate and fine map the big quantitative trait locus (QTL) regions into few markers’ specific regions. A set of putative candidate genes associated with the significant markers were identified and their functions revealed to be directly or indirectly involved in plant defense responses. Genomic prediction revealed reasonable prediction accuracies. The prediction accuracies significantly increased with increasing marker densities and training population size. These results support that MLN is a complex trait controlled by few major and many minor effect genes.

Ancestry-informative marker (AIM) SNP panel for the Malay population

Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise F_ST, informativeness for assignment (I_n), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise F_ST method, while a panel of 200 SNPs selected using I_n and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.