Mendel: the Swiss army knife of genetic analysis programs

Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer

INTRODUCTION

Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex.

METHODS

In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa).

RESULTS

Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer.

CONCLUSION

Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

A Polynesian-specific missense CETP variant alters the lipid profile

Identifying population-specific genetic variants associated with disease and disease-predisposing traits is important to provide insights into the genetic determinants of health and disease between populations, as well as furthering genomic justice. Various common pan-population polymorphisms at CETP associate with serum lipid profiles and cardiovascular disease. Here, sequencing of CETP identified a missense variant rs1597000001 (p.Pro177Leu) specific to Māori and Pacific people that associates with higher HDL-C and lower LDL-C levels. Each copy of the minor allele associated with higher HDL-C by 0.236 mmol/L and lower LDL-C by 0.133 mmol/L. The rs1597000001 effect on HDL-C is comparable with CETP Mendelian loss-of-function mutations that result in CETP deficiency, consistent with our data, which shows that rs1597000001 lowers CETP activity by 27.9%. This study highlights the potential of population-specific genetic analyses for improving equity in genomics and health outcomes for population groups underrepresented in genomic studies.

CanRisk-Prostate: A Comprehensive, Externally Validated Risk Model for the Prediction of Future Prostate Cancer

PURPOSE

Prostate cancer (PCa) is highly heritable. No validated PCa risk model currently exists. We therefore sought to develop a genetic risk model that can provide personalized predicted PCa risks on the basis of known moderate- to high-risk pathogenic variants, low-risk common genetic variants, and explicit cancer family history, and to externally validate the model in an independent prospective cohort.

MATERIALS AND METHODS

We developed a risk model using a kin-cohort comprising individuals from 16,633 PCa families ascertained in the United Kingdom from 1993 to 2017 from the UK Genetic Prostate Cancer Study, and complex segregation analysis adjusting for ascertainment. The model was externally validated in 170,850 unaffected men (7,624 incident PCas) recruited from 2006 to 2010 to the independent UK Biobank prospective cohort study.

RESULTS

The most parsimonious model included the effects of pathogenic variants in BRCA2, HOXB13, and BRCA1, and a polygenic score on the basis of 268 common low-risk variants. Residual familial risk was modeled by a hypothetical recessively inherited variant and a polygenic component whose standard deviation decreased log-linearly with age. The model predicted familial risks that were consistent with those reported in previous observational studies. In the validation cohort, the model discriminated well between unaffected men and men with incident PCas within 5 years (C-index, 0.790; 95% CI, 0.783 to 0.797) and 10 years (C-index, 0.772; 95% CI, 0.768 to 0.777). The 50% of men with highest predicted risks captured 86.3% of PCa cases within 10 years.

CONCLUSION

To our knowledge, this is the first validated risk model offering personalized PCa risks. The model will assist in counseling men concerned about their risk and can facilitate future risk-stratified population screening approaches.

Standing genetic variation affects phenotypic heterogeneity in an SCN5A-mutation founder population with excess sudden cardiac death

BACKGROUND

The Worm Study, ascertained from a multigeneration pedigree segregating a single amino acid deletion in SCN5A (c.4850_4852delTCT, p.(Phe1617del), rs749697698), is characterized by substantial phenotypic heterogeneity and overlap of sudden cardiac death, long-QT syndrome, cardiac conduction disease, Brugada syndrome, and isorhythmic atrioventricular dissociation. Linkage analysis for a synthetic trait derived from these phenotypes identified a single peak (logarithm of the odds [LOD] = 4.52) at the SCN5A/SCN10A/SCN11A locus on chromosome 3.

OBJECTIVE

This study explored the role of additional genetic variation in the chromosome 3 locus as a source of phenotypic heterogeneity in the Worm Study population.

METHODS

Genotypes underlying the linkage peak (n = 70) were characterized using microarrays. Haplotypes were determined using family-aware phasing and a population-specific reference panel. Variants with minor allele frequencies >0.10 were tested for association with cardiac conduction disease and isorhythmic dissociation using LAMP and logistic regression.

RESULTS

Only 1 haplotype carried the p.Phe1617del/rs749697698 deletion, suggesting relatively recent development (∼18 generations); this haplotype contained 5 other missense variants spanning SCN5A/SCN10A/SCN11A. Noncarrier haplotypes (n = 74) ranged in frequency from 0.5% to 5%. Although no variants were associated with cardiac conduction disease, a homozygous missense variant in SCN10A was associated with isorhythmic dissociation after correction for multiple comparisons (odds ratio 11.23; 95% confidence interval 2.76-23.39; P = 1.2 × 10^-4). This variant (rs12632942) was previously associated with PR interval.

CONCLUSION

Our data suggest that other variants, alongside a pathogenic mutation, are associated with phenotypic heterogeneity. Single-mutation screening may be insufficient to predict electrical heart disease in patients and family members. In the Worm Study population, segregating a pathogenic SCN5A mutation, compound variation in the SCN5A/SCN10A/SCN11A locus determines arrhythmic outcome.

Genetic and environmental influences on quality of life: The COVID-19 pandemic as a natural experiment

By treating the coronavirus disease 2019 (COVID-19) pandemic as a natural experiment, we examine the influence of substantial environmental change (i.e., lockdown measures) on individual differences in quality of life (QoL) in the Netherlands. We compare QoL scores before the pandemic (N = 25,772) to QoL scores during the pandemic (N = 17,222) in a sample of twins and their family members. On a 10-point scale, we find a significant decrease in mean QoL from 7.73 (SD = 1.06) before the pandemic to 7.02 (SD = 1.36) during the pandemic (Cohen's d = 0.49). Additionally, variance decomposition shows an increase in unique environmental variance during the pandemic (0.30-1.08), and a decrease in the heritability estimate from 30.9% to 15.5%. We hypothesize that the increased environmental variance is the result of lockdown measures not impacting everybody equally. Whether these effects persist over longer periods and how they impact health inequalities remain topics for future investigation.

Familial Aspects of Mammographic Density Measures Associated with Breast Cancer Risk

Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score.

CREBRF missense variant rs373863828 has both direct and indirect effects on type 2 diabetes and fasting glucose in Polynesian peoples living in Samoa and Aotearoa New Zealand

INTRODUCTION

The minor allele of a missense variant, rs373863828, in CREBRF is associated with higher body mass index (BMI), lower fasting glucose, and lower odds of type 2 diabetes. rs373863828 is common in Pacific Island populations (minor allele frequency (MAF) 0.096-0.259) but rare in non-Pacific Island populations (MAF <0.001). We examined the cross-sectional associations between BMI and rs373863828 in type 2 diabetes and fasting glucose with a large sample of adults of Polynesian ancestries from Samoa, American Samoa, and Aotearoa New Zealand, and estimated the direct and indirect (via BMI) effects of rs373863828 on type 2 diabetes and fasting glucose.

RESEARCH DESIGN AND METHODS

We regressed type 2 diabetes and fasting glucose on BMI and rs373863828 stratified by obesity, regressed type 2 diabetes and fasting glucose on BMI stratified by rs373863828 genotype, and assessed the effects of rs373863828 on type 2 diabetes and fasting glucose with path analysis. The regression analyses were completed separately in four samples that were recruited during different time periods between 1990 and 2010 and then the results were meta-analyzed. All samples were pooled for the path analysis.

RESULTS

Association of BMI with type 2 diabetes and fasting glucose may be greater in those without obesity (OR=7.77, p=0.015 and β=0.213, p=9.53×10^-5, respectively) than in those with obesity (OR=5.01, p=1.12×10^-9 and β=0.162, p=5.63×10^-6, respectively). We did not observe evidence of differences in the association of BMI with type 2 diabetes or fasting glucose by genotype. In the path analysis, the minor allele has direct negative (lower odds of type 2 diabetes and fasting glucose) and indirect positive (higher odds of type 2 diabetes and fasting glucose) effects on type 2 diabetes risk and fasting glucose, with the indirect effects mediated through a direct positive effect of rs373863828 on BMI.

CONCLUSIONS

There may be a stronger effect of BMI on fasting glucose in Polynesian individuals without obesity than in those with obesity. Carrying the rs373863828 minor allele does not decouple higher BMI from higher odds of type 2 diabetes.

Linkage, recombination and mutation rate analyses of 16 X-chromosomal STR loci in Sri Lankan Sinhalese pedigrees

This study investigated genetic linkage, recombination fractions and mutation rates of 16 X chromosomal short tandem repeat (X-STR) markers using a recently developed multiplex PCR assay for Sinhalese population of Sri Lanka, by analyzing 81 three-generation families including 81 grandfathers with daughters and 162 grandsons. In addition, 31 two generation families involving mother father daughter trios were included for mutation analysis. The analysis of recombination fractions between marker pairs identified two linkage blocks (maximum LOD scores > 3.0) each spanning a physical distance of 44.35 Mb and 6.04 Mb respectively. Though recombination events are usually rare among closely linked markers, crossovers were observed for markers located < 1.0 Mb apart. The recombination fractions observed are not fully concordant with those reported earlier, including the second-generation Rutgers combined linkage-physical map. This suggests that linkage is not uniform among different populations. However, the overall and marker-specific mutation rates of the present study did not differ from previous reports, though it needs confirmation with a much larger sample set. The findings presented here will provide the baseline information required for biostatistical calculations conducted using X-STR markers, in complex kinship analysis of Sinhalese population.

Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

Plasmodium vivax malaria is one of the most lethal infectious diseases, with 7 million infections annually. One of the roadblocks to global malaria elimination is the lack of highly sensitive, specific, and accurate diagnostic tools. The absence of diagnostic tools in particular has led to poor differentiation among parasite species, poor prognosis, and delayed treatment. The improvement necessary in diagnostic tools can be broadly grouped into two categories: technologies-driven and omics-driven progress over time. This article discusses the recent advancement in omics-based malaria for identifying the next generation biomarkers for a highly sensitive and specific assay with a rapid and antecedent prognosis of the disease. We summarize the state-of-the-art diagnostic technologies, the key challenges, opportunities, and emerging prospects of multi-omics-based sensors.

A new Bayesian approach for QTL mapping of family data

In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs' effects. The model considers the detailed familiar dependence and it does not rely on random effects. It combines the probability for Mendelian inheritance of parents' genotype and the correlation between flanking markers and QTLs. This is an advance when compared with models which use only Mendelian segregation or only the correlation between markers and QTLs to estimate transmission probabilities. We use the Bayesian approach to estimate the number of QTLs, their location and the additive and dominance effects. We compare the performance of the proposed method with variance component and LASSO models using simulated and GAW17 data sets. Under tested conditions, the proposed method outperforms other methods in aspects such as estimating the number of QTLs, the accuracy of the QTLs' position and the estimate of their effects. The results of the application of the proposed method to data sets exceeded all of our expectations.

Major Depressive Disorder and Lifestyle: Correlated Genetic Effects in Extended Twin Pedigrees

In recent years, evidence has accumulated with regard to the ubiquity of pleiotropy across the genome, and shared genetic etiology is thought to play a large role in the widespread comorbidity among psychiatric disorders and risk factors. Recent methods investigate pleiotropy by estimating genetic correlation from genome-wide association summary statistics. More comprehensive estimates can be derived from the known relatedness between genetic relatives. Analysis of extended twin pedigree data allows for the estimation of genetic correlation for additive and non-additive genetic effects, as well as a shared household effect. Here we conduct a series of bivariate genetic analyses in extended twin pedigree data on lifetime major depressive disorder (MDD) and three indicators of lifestyle, namely smoking behavior, physical inactivity, and obesity, decomposing phenotypic variance and covariance into genetic and environmental components. We analyze lifetime MDD and lifestyle data in a large multigenerational dataset of 19,496 individuals by variance component analysis in the ‘Mendel’ software. We find genetic correlations for MDD and smoking behavior (r_G = 0.249), physical inactivity (r_G = 0.161), body-mass index (r_G = 0.081), and obesity (r_G = 0.155), which were primarily driven by additive genetic effects. These outcomes provide evidence in favor of a shared genetic etiology between MDD and the lifestyle factors.

Risk of Pancreatic Cancer Among Individuals With Pathogenic Variants in the ATM Gene

Importance

Pathogenic germline variants in the ATM gene have been associated with pancreatic cancer risk. Although genetic testing identifies these variants in approximately 1% to 3% of unselected patients with pancreatic cancer, the lifetime risk of pancreatic cancer among individuals with pathogenic ATM variants has not been well estimated.

Objective

To estimate age-specific penetrance of pancreatic cancer in individuals with a pathogenic variant in the ATM gene.

Design, Setting, and Participants

This was a multicenter cohort study of pancreatic cancer family registries in the US and Canada using pedigree data from 130 pancreatic cancer kindreds with a pathogenic germline ATM variant. Data analyses were performed from January 2020 to February 2021.

Main Outcomes and Measures

Observational age-specific risk of pancreatic cancer. Penetrance was estimated using modified segregation analysis.

Results

The study population of 130 families (123 [95%] White families) comprised 2227 family members (mean age [SD], 58 [22] years; 1096 [49%] women) with complete records (ie, including familial relationships, pancreatic cancer diagnosis, ATM status, proband status, and age), of which 155 individuals had positive results for an ATM pathogenic variant, 16 had a negative result, and the remainder did not have a test result. In these 130 families, 217 individuals had pancreatic cancer: 78 families had 1 such member; 34 families had 2 such members; and 18 families had 3 or more members with pancreatic cancer. The average (range) age at diagnosis was 64 (31-98) years. The cumulative risk of pancreatic cancer among individuals with a germline pathogenic ATM variant was estimated to be 1.1% (95% CI, 0.8%-1.3%) by age 50 years; 6.3% (95% CI, 3.9%-8.7%) by age 70 years; and 9.5% (95% CI, 5.0%-14.0%) by age 80 years. Overall, the relative risk of pancreatic cancer was 6.5 (95% CI, 4.5-9.5) in ATM variant carriers compared with noncarriers.

Conclusions and Relevance

This multicenter cohort study found that individuals with a germline pathogenic ATM variant were at an increased lifetime risk of pancreatic cancer. These risk estimates can help guide decision-making when evaluating the risks and benefits of enhanced early detection surveillance.

Cumulative risks of colorectal cancer in Han Chinese patients with Lynch syndrome in Taiwan

Patients with Lynch syndrome have a high risk of colorectal cancer (CRC). In this study, we estimated the age- and sex-specific cumulative risks of CRC in Han Chinese patients with Lynch syndrome caused by the pathogenic germline mutations in MLH1 or MSH2 in Taiwan. Based on 321 mutation carriers and 419 non-mutation carriers from 75 pedigrees collected in an Amsterdam criteria family registry in Taiwan, the age- and sex-specific cumulative risks of CRC in male carriers of mutation in MLH1 and MSH2 at the age of 70 years were 60.3% (95% confidence interval (CI) = 31.1%–89.9%) and 76.7% (95% CI = 37.2%–99.0%), respectively. For females, the cumulative risks of CRC at the age of 70 were estimated to be 30.6% (95% CI = 14.3%–57.7%) and 49.3% (95% CI = 21.9%–84.5%) in the carriers of MLH1 and MSH2 germline mutations, respectively. In conclusion, the cumulative risks of CRC at the age of 70 in the Han Chinese patients is higher in mutation carriers than non-mutation carriers and male mutation carriers have a higher cumulative risk of developing CRC than the female mutation carriers.

The Heritability of Type D Personality by an Extended Twin-Pedigree Analysis in the Netherlands Twin Register

Type D (Distressed) personality combines negative affectivity (NA) and social inhibition (SI) and is associated with an increased risk of cardiovascular disease. We aimed to (1) validate a new proxy based on the Achenbach System of Empirically Based Assessment (ASEBA) for Type D personality and its NA and SI subcomponents and (2) estimate the heritability of the Type D proxy in an extended twin-pedigree design in the Netherlands Twin Register (NTR). Proxies for the dichotomous Type D classification, and continuous NA, SI, and NAxSI (the continuous measure of Type D) scales were created based on 12 ASEBA items for 30,433 NTR participants (16,449 twins and 13,984 relatives from 11,106 pedigrees) and sources of variation were analyzed in the ‘Mendel’ software package. We estimated additive and non-additive genetic variance components, shared household and unique environmental variance components and ran bivariate models to estimate the genetic and non-genetic covariance between NA and SI. The Type D proxy showed good reliability and construct validity. The best fitting genetic model included additive and non-additive genetic effects with broad-sense heritabilities for NA, SI and NAxSI estimated at 49%, 50% and 49%, respectively. Household effects showed small contributions (4–9%) to the total phenotypic variation. The genetic correlation between NA and SI was .66 (reflecting both additive and non-additive genetic components). Thus, Type D personality and its NA and SI subcomponents are heritable, with a shared genetic basis for the two subcomponents.

Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity

BACKGROUND

Consecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression.

RESULTS

We extend and efficiently implement iterative hard thresholding (IHT) for multiple regression, treating all SNPs simultaneously. Our extensions accommodate generalized linear models, prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing and exhibits a 2-3 orders of magnitude decrease in false-positive rates compared with lasso regression. We also test IHT on the UK Biobank hypertension phenotypes and the Northern Finland Birth Cohort of 1966 cardiovascular phenotypes. We find that IHT scales to the large datasets of contemporary human genetics and recovers the plausible genetic variants identified by previous studies.

CONCLUSIONS

Our real data analysis and simulation studies suggest that IHT can (i) recover highly correlated predictors, (ii) avoid over-fitting, (iii) deliver better true-positive and false-positive rates than either marginal testing or lasso regression, (iv) recover unbiased regression coefficients, (v) exploit prior information and group-sparsity, and (vi) be used with biobank-sized datasets. Although these advances are studied for genome-wide association studies inference, our extensions are pertinent to other regression problems with large numbers of predictors.

Measurement and genetic architecture of lifetime depression in the Netherlands as assessed by LIDAS (Lifetime Depression Assessment Self-report)

BACKGROUND

Major depressive disorder (MDD) is a common mood disorder, with a heritability of around 34%. Molecular genetic studies made significant progress and identified genetic markers associated with the risk of MDD; however, progress is slowed down by substantial heterogeneity as MDD is assessed differently across international cohorts. Here, we used a standardized online approach to measure MDD in multiple cohorts in the Netherlands and evaluated whether this approach can be used in epidemiological and genetic association studies of depression.

METHODS

Within the Biobank Netherlands Internet Collaboration (BIONIC) project, we collected MDD data in eight cohorts involving 31 936 participants, using the online Lifetime Depression Assessment Self-report (LIDAS), and estimated the prevalence of current and lifetime MDD in 22 623 unrelated individuals. In a large Netherlands Twin Register (NTR) twin-family dataset (n ≈ 18 000), we estimated the heritability of MDD, and the prediction of MDD in a subset (n = 4782) through Polygenic Risk Score (PRS).

RESULTS

Estimates of current and lifetime MDD prevalence were 6.7% and 18.1%, respectively, in line with population estimates based on validated psychiatric interviews. In the NTR heritability estimates were 0.34/0.30 (s.e. = 0.02/0.02) for current/lifetime MDD, respectively, showing that the LIDAS gives similar heritability rates for MDD as reported in the literature. The PRS predicted risk of MDD (OR 1.23, 95% CI 1.15-1.32, R2 = 1.47%).

CONCLUSIONS

By assessing MDD status in the Netherlands using the LIDAS instrument, we were able to confirm previously reported MDD prevalence and heritability estimates, which suggests that this instrument can be used in epidemiological and genetic association studies of depression.

The derived allele of a novel intergenic variant at chromosome 11 associates with lower body mass index and a favorable metabolic phenotype in Greenlanders

The genetic architecture of the small and isolated Greenlandic population is advantageous for identification of novel genetic variants associated with cardio-metabolic traits. We aimed to identify genetic loci associated with body mass index (BMI), to expand the knowledge of the genetic and biological mechanisms underlying obesity. Stage 1 BMI-association analyses were performed in 4,626 Greenlanders. Stage 2 replication and meta-analysis were performed in additional cohorts comprising 1,058 Yup'ik Alaska Native people, and 1,529 Greenlanders. Obesity-related traits were assessed in the stage 1 study population. We identified a common variant on chromosome 11, rs4936356, where the derived G-allele had a frequency of 24% in the stage 1 study population. The derived allele was genome-wide significantly associated with lower BMI (beta (SE), -0.14 SD (0.03), p = 3.2x10-8), corresponding to 0.64 kg/m2 lower BMI per G allele in the stage 1 study population. We observed a similar effect in the Yup'ik cohort (-0.09 SD, p = 0.038), and a non-significant effect in the same direction in the independent Greenlandic stage 2 cohort (-0.03 SD, p = 0.514). The association remained genome-wide significant in meta-analysis of the Arctic cohorts (-0.10 SD (0.02), p = 4.7x10-8). Moreover, the variant was associated with a leaner body type (weight, -1.68 (0.37) kg; waist circumference, -1.52 (0.33) cm; hip circumference, -0.85 (0.24) cm; lean mass, -0.84 (0.19) kg; fat mass and percent, -1.66 (0.33) kg and -1.39 (0.27) %; visceral adipose tissue, -0.30 (0.07) cm; subcutaneous adipose tissue, -0.16 (0.05) cm, all p<0.0002), lower insulin resistance (HOMA-IR, -0.12 (0.04), p = 0.00021), and favorable lipid levels (triglyceride, -0.05 (0.02) mmol/l, p = 0.025; HDL-cholesterol, 0.04 (0.01) mmol/l, p = 0.0015). In conclusion, we identified a novel variant, where the derived G-allele possibly associated with lower BMI in Arctic populations, and as a consequence also leaner body type, lower insulin resistance, and a favorable lipid profile.

An Extended Twin-Pedigree Study of Different Classes of Voluntary Exercise Behavior

We investigated the familial clustering of different classes of voluntary regular exercise behavior in extended twin-family pedigrees. In contrast to the earlier work based on twin data only, this allowed us to estimate the contributions of shared household effects (C), additive (A), and non-additive (D) genetic effects on voluntary exercise behavior. To test whether shared household effects were inflated by assortative mating we examined the causes of spousal resemblance. For adolescent and adult participants (aged 16 to 65) in the Netherlands Twin Register we constructed 19,543 pedigrees which specified all relations among nuclear family members and larger families in the register (N = 50,690 individuals). Data were available on total weekly MET minutes spent on leisure time exercise, and on total weekly MET minutes spent on exercise activities in team-based, solitary, competitive, non-competitive, externally paced and internally paced exercise. We analyzed the data in the Mendel software package (Lange et al. in Bioinformatics 29(12):1568–1570, 2013) under multiple definitions of household sharing and used data from spouses of twins to test phenotypic assortment, social homogamy, and marital interaction as potential sources of spousal resemblance. Results confirmed the influence of genetic factors on the total volume of weekly exercise behavior throughout the life span. Broad sense heritability ranged from 34 to 41% (19–26% A, 12–21% D), and did not depend on the definition for household sharing. Engaging in team-based, competitive, externally paced activities (e.g., soccer) was ~ 13% more heritable than engaging in non-competitive, solitary activities (e.g., jogging). Having shared a household as siblings explained 4–8% of the variance in adult exercise behavior, whereas sharing a household by spouses yielded higher C estimates (20–24%), as it incorporates spousal resemblance. Spousal resemblance was explained by both social homogamy and marital interaction, with little evidence for phenotypic assortment. We conclude that both the amount of voluntary exercise behavior and the preference for specific classes of exercise activities in adults is explained by additive and non-additive genetic factors and unique environmental influences that include correlated exercise behavior of spouses.

Heritability of interpack aggression in a wild pedigreed population of North American grey wolves

Aggression is a quantitative trait deeply entwined with individual fitness. Mapping the genomic architecture underlying such traits is complicated by complex inheritance patterns, social structure, pedigree information and gene pleiotropy. Here, we leveraged the pedigree of a reintroduced population of grey wolves (Canis lupus) in Yellowstone National Park, Wyoming, USA, to examine the heritability of and the genetic variation associated with aggression. Since their reintroduction, many ecological and behavioural aspects have been documented, providing unmatched records of aggressive behaviour across multiple generations of a wild population of wolves. Using a linear mixed model, a robust genetic relationship matrix, 12,288 single nucleotide polymorphisms (SNPs) and 111 wolves, we estimated the SNP-based heritability of aggression to be 37% and an additional 14% of the phenotypic variation explained by shared environmental exposures. We identified 598 SNP genotypes from 425 grey wolves to resolve a consensus pedigree that was included in a heritability analysis of 141 individuals with SNP genotype, metadata and aggression data. The pedigree-based heritability estimate for aggression is 14%, and an additional 16% of the phenotypic variation was explained by shared environmental exposures. We find strong effects of breeding status and relative pack size on aggression. Through an integrative approach, these results provide a framework for understanding the genetic architecture of a complex trait that influences individual fitness, with linkages to reproduction, in a social carnivore. Along with a few other studies, we show here the incredible utility of a pedigreed natural population for dissecting a complex, fitness-related behavioural trait.

Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale

Logistic regression is the primary analysis tool for binary traits in genome-wide association studies (GWAS). Multinomial regression extends logistic regression to multiple categories. However, many phenotypes more naturally take ordered, discrete values. Examples include (a) subtypes defined from multiple sources of clinical information and (b) derived phenotypes generated by specific phenotyping algorithms for electronic health records (EHR). GWAS of ordinal traits have been problematic. Dichotomizing can lead to a range of arbitrary cutoff values, generating inconsistent, hard to interpret results. Using multinomial regression ignores trait value hierarchy and potentially loses power. Treating ordinal data as quantitative can lead to misleading inference. To address these issues, we analyze ordinal traits with an ordered, multinomial model. This approach increases power and leads to more interpretable results. We derive efficient algorithms for computing test statistics, making ordinal trait GWAS computationally practical for Biobank scale data. Our method is available as a Julia package OrdinalGWAS.jl. Application to a COPDGene study confirms previously found signals based on binary case-control status, but with more significance. Additionally, we demonstrate the capability of our package to run on UK Biobank data by analyzing hypertension as an ordinal trait.

Phylogeography, more than elevation, accounts for sex chromosome differentiation in Swiss populations of the common frog (Rana temporaria)

Sex chromosomes in vertebrates range from highly heteromorphic (as in most birds and mammals) to strictly homomorphic (as in many fishes, amphibians, and nonavian reptiles). Reasons for these contrasted evolutionary trajectories remain unclear, but species such as common frogs with polymorphism in the extent of sex chromosome differentiation may potentially deliver important clues. By investigating 92 common frog populations from a wide range of elevations throughout Switzerland, we show that sex chromosome differentiation strongly correlates with alleles at the candidate sex-determining gene Dmrt1. Y-specific Dmrt1 haplotypes cluster into two main haplogroups, Y_A and Y_B , with a phylogeographic signal that parallels mtDNA haplotypes: Y_A populations, with mostly well-differentiated sex chromosomes, occur primarily south of the main alpine ridge that bisects Switzerland, whereas Y_B populations, with mostly undifferentiated (proto-)sex chromosomes, occur north of this ridge. Elevation has only a marginal effect, opposing previous suggestions of a major role for climate on sex chromosome differentiation. The Y-haplotype effect might result from differences in the penetrance of alleles at the sex-determining locus (such that sex reversal and ensuing X-Y recombination are more frequent in Y_B populations), and/or fixation of an inversion on Y_A (as supported by the empirical observation that Y_A haplotypes might not recombine in XY_A females).

Mitochondrial DNA variation in Sub-Saharan Africa: Forensic data from a mixed West African sample, Côte d'Ivoire (Ivory Coast), and Rwanda

This study provides 398 novel complete mitochondrial control region sequences that augment the still underrepresented data from Africa by three datasets: a mixed West African sample set deriving from 12 countries (n = 145) and datasets from Côte d'Ivoire (Ivory Coast) (n = 100) as well as Rwanda (n = 153). The analysis of mtDNA variation and genetic comparisons with published data revealed low random match probabilities in all three datasets and typical West African and East African diversity, respectively. Genetic parameters indicate that the presented mixed West African dataset may serve as first forensic mtDNA control region database for West Africa in general. In addition, a strategy for responsible forensic application of precious mtDNA population samples potentially containing close maternal relatives is outlined. The datasets will be uploaded to the forensic mtDNA database EMPOP (https://empop.online) upon publication.

Clinical features and cancer risk in families with pathogenic CDH1 variants irrespective of clinical criteria

Background

The clinical phenotype of CDH1 pathogenic variant carriers has mostly been studied in families that fulfil criteria of hereditary diffuse gastric cancer (HDGC). We aimed at determining cancer phenotype and cancer risk estimation among families with CDH1 pathogenic variants not selected by HDGC clinical criteria.

Methods

Patients were all consecutively identified CDH1 pathogenic variant carriers from a clinical laboratory tested with multigene panel testing and from an academic cancer genetics programme. Clinical and demographic features, cancer phenotypes and genotype–phenotype correlations were determined among CDH1 families. Age-specific cumulative cancer risks (penetrance) were calculated based on 38 families with available pedigrees.

Results

Within the 113 CDH1 pathogenic variant probands and 476 relatives, 113 had gastric cancer, 177 breast cancer and 196 other cancers. Mean age at diagnosis was 47 for gastric and 54 for breast cancer. Forty-six per cent fulfilled criteria of HDGC. While 36% of families had both gastric and breast cancers, 36% had breast but no gastric cancers and 16% had gastric but not breast cancers. Cumulative risk of cancer by age 80 was 37.2% for gastric and 42.9% for breast cancer.

Conclusion

In unselected CDH1 pathogenic variant carrier families, gastric cancer risks were lower and age at diagnosis higher than previously reported in families pre-selected for HDGC criteria. A substantial proportion of families did not present with any gastric cancers and their cancers were limited to breast. Thus, clinical criteria for CDH1 testing should be widened, including breast cancer families only, and a consideration for delayed prophylactic gastrectomy/surveillance should be evaluated.

Homeobox B13 G84E Mutation and Prostate Cancer Risk

BACKGROUND

The homeobox B13 (HOXB13) G84E mutation has been recommended for use in genetic counselling for prostate cancer (PCa), but the magnitude of PCa risk conferred by this mutation is uncertain.

OBJECTIVE

To obtain precise risk estimates for mutation carriers and information on how these vary by family history and other factors.

DESIGN, SETTING, AND PARTICIPANTS

Two-fold: a systematic review and meta-analysis of published risk estimates, and a kin-cohort study comprising pedigree data on 11983 PCa patients enrolled during 1993-2014 from 189 UK hospitals and who had been genotyped for HOXB13 G84E.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Relative and absolute PCa risks. Complex segregation analysis with ascertainment adjustment to derive age-specific risks applicable to the population, and to investigate how these vary by family history and birth cohort.

RESULTS AND LIMITATIONS

A meta-analysis of case-control studies revealed significant heterogeneity between reported relative risks (RRs; range: 0.95-33.0, p<0.001) and differences by case selection (p=0.007). Based on case-control studies unselected for PCa family history, the pooled RR estimate was 3.43 (95% confidence interval [CI] 2.78-4.23). In the kin-cohort study, PCa risk for mutation carriers varied by family history (p<0.001). There was a suggestion that RRs decrease with age, but this was not significant (p=0.068). We found higher RR estimates for men from more recent birth cohorts (p=0.004): 3.09 (95% CI 2.03-4.71) for men born in 1929 or earlier and 5.96 (95% CI 4.01-8.88) for men born in 1930 or later. The absolute PCa risk by age 85 for a male HOXB13 G84E carrier varied from 60% for those with no PCa family history to 98% for those with two relatives diagnosed at young ages, compared with an average risk of 15% for noncarriers. Limitations include the reliance on self-reported cancer family history.

CONCLUSIONS

PCa risks for HOXB13 G84E mutation carriers are heterogeneous. Counselling should not be based on average risk estimates but on age-specific absolute risk estimates tailored to individual mutation carriers' family history and birth cohort.

PATIENT SUMMARY

Men who carry a hereditary mutation in the homeobox B13 (HOXB13) gene have a higher than average risk for developing prostate cancer. In our study, we examined a large number of families of men with prostate cancer recruited across UK hospitals, to assess what other factors may contribute to this risk and to assess whether we could create a precise model to help in predicting a man's prostate cancer risk. We found that the risk of developing prostate cancer in men who carry this genetic mutation is also affected by a family history of prostate cancer and their year of birth. This information can be used to assess more personalised prostate cancer risks to men who carry HOXB13 mutations and hence better counsel them on more personalised risk management options, such as tailoring prostate cancer screening frequency.

Noncoding Variations in the Gene Encoding Ceramide Synthase 6 are Associated with Type 2 Diabetes in a Large Indigenous Australian Pedigree

Type 2 diabetes (T2D) is a chronic disease that disproportionately affects Indigenous Australians. We have previously reported the localization of a novel T2D locus by linkage analysis to chromosome 2q24 in a large admixed Indigenous Australian pedigree (Busfield et al. (2002). American Journal of Human Genetics, 70, 349-357). Here we describe fine mapping of this region in this pedigree, with the identification of SNPs showing strong association with T2D: rs3845724 (diabetes p = 7 × 10-4), rs4668106 (diabetes p = 9 × 10-4) and rs529002 (plasma glucose p = 3 × 10-4). These associations were successfully replicated in an independent collection of Indigenous Australian T2D cases and controls. These SNPs all lie within the gene encoding ceramide synthase 6 (CERS6) and thus may regulate ceramide synthesis.

OPENMENDEL: a cooperative programming project for statistical genetics

Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.

Genetic linkage analysis of a large family identifies FIGN as a candidate modulator of reduced penetrance in heritable pulmonary arterial hypertension

BACKGROUND

Mapping the genetic component of molecular mechanisms responsible for the reduced penetrance (RP) of rare disorders constitutes one of the most challenging problems in human genetics. Heritable pulmonary arterial hypertension (PAH) is one such disorder characterised by rare mutations mostly occurring in the bone morphogenetic protein receptor type 2 (BMPR2) gene and a wide heterogeneity of penetrance modifier mechanisms. Here, we analyse 32 genotyped individuals from a large Iberian family of 65 members, including 22 carriers of the pathogenic BMPR2 mutation c.1472G>A (p.Arg491Gln), 8 of them diagnosed with PAH by right-heart catheterisation, leading to an RP rate of 36.4%.

METHODS

We performed a linkage analysis on the genotyping data to search for genetic modifiers of penetrance. Using functional genomics data, we characterised the candidate region identified by linkage analysis. We also predicted the haplotype segregation within the family.

RESULTS

We identified a candidate chromosome region in 2q24.3, 38 Mb upstream from BMPR2, with significant linkage (LOD=4.09) under a PAH susceptibility model. This region contains common variants associated with vascular aetiology and shows functional evidence that the putative genetic modifier is located in the upstream distal promoter of the fidgetin (FIGN) gene.

CONCLUSION

Our results suggest that the genetic modifier acts through FIGN transcriptional regulation, whose expression variability would contribute to modulating heritable PAH. This finding may help to advance our understanding of RP in PAH across families sharing the p.Arg491Gln pathogenic mutation in BMPR2.

MM Algorithms For Variance Components Models

Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generations of statisticians and numerical analysts, maximum likelihood estimation and restricted maximum likelihood estimation of variance component models remain numerically challenging. Building on the minorization-maximization (MM) principle, this paper presents a novel iterative algorithm for variance components estimation. Our MM algorithm is trivial to implement and competitive on large data problems. The algorithm readily extends to more complicated problems such as linear mixed models, multivariate response models possibly with missing data, maximum a posteriori estimation, and penalized estimation. We establish the global convergence of the MM algorithm to a Karush-Kuhn-Tucker (KKT) point and demonstrate, both numerically and theoretically, that it converges faster than the classical EM algorithm when the number of variance components is greater than two and all covariance matrices are positive definite.

A novel association of rs13334070 in the RPGRIP1L gene with adiposity factors discovered by joint linkage and linkage disequilibrium analysis in Iranian pedigrees: Tehran Cardiometabolic Genetic Study (TCGS)

Understanding the genetic and metabolic bases of obesity is helpful in planning and developing health strategies. Therefore, the first family-based joint linkage and linkage disequilibrium study was conducted in Iranian pedigrees to assess the relationship between obesity and single-nucleotide polymorphisms (SNPs) located in the 16q12.2 region. In the present study, a total of 13,344 individuals were included, of whom 12,502 individuals were within 3,109 pedigrees and 842 were unrelated singletons. To investigate the relationship between obesity and genetic variants, a joint model of linkage and linkage disequilibrium was applied. Moreover, a sequence kernel association test (SKAT) was used to evaluate the association of the SNP set with body size and lipid profile measurements. The joint model showed that rs13334070, in the intron 4 of the RPGRIP1L gene, has a significant association with obesity. According to the 4-gamete rule, which is a procedure for constructing SNP sets by considering recombination occurrence between SNPs, this polymorphism has a high correlation with six nearby SNPs that make an SNP set. SKAT showed that this SNP set has a significant association with body size factors, but almost no association with most of the lipid profile measurements. In conclusion, from the result of this study, it might be reasonable to consider RPGRIP1L as an important gene whose variations could be associated with obesity risk factors.

Non-parametric estimation of survival in age-dependent genetic disease and application to the transthyretin-related hereditary amyloidosis

In genetic diseases with variable age of onset, survival function estimation for the mutation carriers as well as estimation of the modifying factors effects are essential to provide individual risk assessment, both for mutation carriers management and prevention strategies. In practice, this survival function is classically estimated from pedigrees data where most genotypes are unobserved. In this article, we present a unifying Expectation-Maximization (EM) framework combining probabilistic computations in Bayesian networks with standard statistical survival procedures in order to provide mutation carrier survival estimates. The proposed approach allows to obtain previously published parametric estimates (e.g. Weibull survival) as particular cases as well as more general Kaplan-Meier non-parametric estimates, which is the main contribution. Note that covariates can also be taken into account using a proportional hazard model. The whole methodology is both validated on simulated data and applied to family samples with transthyretin-related hereditary amyloidosis (a rare autosomal dominant disease with highly variable age of onset), showing very promising results.

Whole exome sequencing of ENU-induced thrombosis modifier mutations in the mouse

Although the Factor V Leiden (FVL) gene variant is the most prevalent genetic risk factor for venous thrombosis, only 10% of FVL carriers will experience such an event in their lifetime. To identify potential FVL modifier genes contributing to this incomplete penetrance, we took advantage of a perinatal synthetic lethal thrombosis phenotype in mice homozygous for FVL (F5L/L) and haploinsufficient for tissue factor pathway inhibitor (Tfpi+/-) to perform a sensitized dominant ENU mutagenesis screen. Linkage analysis conducted in the 3 largest pedigrees generated from the surviving F5L/L Tfpi+/- mice ('rescues') using ENU-induced coding variants as genetic markers was unsuccessful in identifying major suppressor loci. Whole exome sequencing was applied to DNA from 107 rescue mice to identify candidate genes enriched for ENU mutations. A total of 3,481 potentially deleterious candidate ENU variants were identified in 2,984 genes. After correcting for gene size and multiple testing, Arl6ip5 was identified as the most enriched gene, though not reaching genome-wide significance. Evaluation of CRISPR/Cas9 induced loss of function in the top 6 genes failed to demonstrate a clear rescue phenotype. However, a maternally inherited (not ENU-induced) de novo mutation (Plcb4R335Q) exhibited significant co-segregation with the rescue phenotype (p = 0.003) in the corresponding pedigree. Thrombosis suppression by heterozygous Plcb4 loss of function was confirmed through analysis of an independent, CRISPR/Cas9-induced Plcb4 mutation (p = 0.01).

Identification of novel high-impact recessively inherited type 2 diabetes risk variants in the Greenlandic population

AIMS/HYPOTHESIS

In a recent study using a standard additive genetic model, we identified a TBC1D4 loss-of-function variant with a large recessive impact on risk of type 2 diabetes in Greenlanders. The aim of the current study was to identify additional genetic variation underlying type 2 diabetes using a recessive genetic model, thereby increasing the power to detect variants with recessive effects.

METHODS

We investigated three cohorts of Greenlanders (B99, n = 1401; IHIT, n = 3115; and BBH, n = 547), which were genotyped using Illumina MetaboChip. Of the 4674 genotyped individuals passing quality control, 4648 had phenotype data available, and type 2 diabetes association analyses were performed for 317 individuals with type 2 diabetes and 2631 participants with normal glucose tolerance. Statistical association analyses were performed using a linear mixed model.

RESULTS

Using a recessive genetic model, we identified two novel loci associated with type 2 diabetes in Greenlanders, namely rs870992 in ITGA1 on chromosome 5 (OR 2.79, p = 1.8 × 10-8), and rs16993330 upstream of LARGE1 on chromosome 22 (OR 3.52, p = 1.3 × 10-7). The LARGE1 variant did not reach the conventional threshold for genome-wide significance (p < 5 × 10-8) but did withstand a study-wide Bonferroni-corrected significance threshold. Both variants were common in Greenlanders, with minor allele frequencies of 23% and 16%, respectively, and were estimated to have large recessive effects on risk of type 2 diabetes in Greenlanders, compared with additively inherited variants previously observed in European populations.

CONCLUSIONS/INTERPRETATION

We demonstrate the value of using a recessive genetic model in a historically small and isolated population to identify genetic risk variants. Our findings give new insights into the genetic architecture of type 2 diabetes, and further support the existence of high-effect genetic risk factors of potential clinical relevance, particularly in isolated populations.

DATA AVAILABILITY

The Greenlandic MetaboChip-genotype data are available at European Genome-Phenome Archive (EGA; https://ega-archive.org/ ) under the accession EGAS00001002641.

Long-range genomic regulators of THBS1 and LTBP4 modify disease severity in duchenne muscular dystrophy

OBJECTIVE

Duchenne muscular dystrophy (DMD) is a severe X-linked recessive disease caused by loss-of-function dystrophin (DMD) mutations in boys, who typically suffer loss of ambulation by age 12. Previously, we reported that coding variants in latent transforming growth factor beta (TGFβ)-binding protein 4 (LTBP4) were associated with reduced TGFβ signaling and prolonged ambulation (p = 1.0 × 10-3 ) in DMD patients; this result was subsequently replicated by other groups. In this study, we evaluated whether additional DMD modifier genes are observed using whole-genome association in the original cohort.

METHODS

We performed a genome-wide association study (GWAS) for single-nucleotide polymorphisms (SNPs) influencing loss of ambulation (LOA) in the same cohort of 253 DMD patients used to detect the candidate association with LTBP4 coding variants. Gene expression and chromatin interaction databases were used to fine-map association signals above the threshold for genome-wide significance.

RESULTS

Despite the small sample size, two loci associated with prolonged ambulation met genome-wide significance and were tagged by rs2725797 (chr15, p = 6.6 × 10-9 ) and rs710160 (chr19, p = 4.7 × 10-8 ). Gene expression and chromatin interaction data indicated that the latter SNP tags regulatory variants of LTBP4, whereas the former SNP tags regulatory variants of thrombospondin-1 (THBS1): an activator of TGFβ signaling by direct binding to LTBP4 and an inhibitor of proangiogenic nitric oxide signaling.

INTERPRETATION

Together with previous evidence implicating LTBP4, the THBS1 modifier locus emphasizes the role that common regulatory variants in gene interaction networks can play in mitigating disease progression in muscular dystrophy. Ann Neurol 2018;84:234-245.

Validation of a microRNA target site polymorphism in H3F3B that is potentially associated with a broad schizophrenia phenotype

Despite much progress, few genetic findings for schizophrenia have been assessed by functional validation experiments at the molecular level. We previously reported evidence for genetic linkage of broadly defined schizophrenia to chromosome 17q25 in a sample of 24 multiplex families. 2,002 SNPs under this linkage peak were analyzed for evidence of linkage disequilibrium using the posterior probability of linkage (PPL) framework. SNP rs1060120 produced the strongest evidence for association, with a PPLD|L score of 0.21. This SNP is located within the 3'UTR of the histone gene H3F3B and colocalizes with potential gene target miR-616. A custom miRNA target prediction program predicted that the binding of miR-616 to H3F3B transcripts would be altered by the allelic variants of rs1060120. We used dual luciferase assays to experimentally validate this interaction. The rs1060120 A allele significantly reduced luciferase expression, indicating a stronger interaction with miR-616 than the G allele (p = 0.000412). These results provide functional validation that this SNP could alter schizophrenia epigenetic mechanisms thereby contributing to schizophrenia-related disease risk.

Adjusting for Familial Relatedness in the Analysis of GWAS Data

Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.

Computing Individual Risks Based on Family History in Genetic Disease in the Presence of Competing Risks

When considering a genetic disease with variable age at onset (e.g., familial amyloid neuropathy, cancers), computing the individual risk of the disease based on family history (FH) is of critical interest for both clinicians and patients. Such a risk is very challenging to compute because (1) the genotype X of the individual of interest is in general unknown, (2) the posterior distribution ℙ(X∣FH, T > t) changes with t (T is the age at disease onset for the targeted individual), and (3) the competing risk of death is not negligible. In this work, we present modeling of this problem using a Bayesian network mixed with (right-censored) survival outcomes where hazard rates only depend on the genotype of each individual. We explain how belief propagation can be used to obtain posterior distribution of genotypes given the FH and how to obtain a time-dependent posterior hazard rate for any individual in the pedigree. Finally, we use this posterior hazard rate to compute individual risk, with or without the competing risk of death. Our method is illustrated using the Claus-Easton model for breast cancer. The competing risk of death is derived from the national French registry.

An Extended Twin-Pedigree Study of Neuroticism in the Netherlands Twin Register

For the participants in the Netherlands Twin Register (NTR) we constructed the extended pedigrees which specify all relations among nuclear and larger twin families in the register. A total of 253,015 subjects from 58,645 families were linked to each other, to the degree that we had information on the relations among participants. We describe the algorithm that was applied to construct the pedigrees. For > 30,000 adolescent and adult NTR participants data were available on harmonized neuroticism scores. We analyzed these data in the Mendel software package (Lange et al., Bioinformatics 29(12):1568-1570, 2013) to estimate the contributions of additive and non-additive genetic factors. In contrast to much of the earlier work based on twin data rather than on extended pedigrees, we could also estimate the contribution of shared household effects in the presence of non-additive genetic factors. The estimated broad-sense heritability of neuroticism was 47%, with almost equal contributions of additive and non-additive (dominance) genetic factors. A shared household effect explained 13% and unique environmental factors explained the remaining 40% of the variance in neuroticism.

Iterative hard thresholding for model selection in genome-wide association studies

A genome-wide association study (GWAS) correlates marker and trait variation in a study sample. Each subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here, we assume that subjects are randomly collected unrelateds and that trait values are normally distributed or can be transformed to normality. Over the past decade, geneticists have been remarkably successful in applying GWAS analysis to hundreds of traits. The massive amount of data produced in these studies present unique computational challenges. Penalized regression with the ℓ₁ penalty (LASSO) or minimax concave penalty (MCP) penalties is capable of selecting a handful of associated SNPs from millions of potential SNPs. Unfortunately, model selection can be corrupted by false positives and false negatives, obscuring the genetic underpinning of a trait. Here, we compare LASSO and MCP penalized regression to iterative hard thresholding (IHT). On GWAS regression data, IHT is better at model selection and comparable in speed to both methods of penalized regression. This conclusion holds for both simulated and real GWAS data. IHT fosters parallelization and scales well in problems with large numbers of causal markers. Our parallel implementation of IHT accommodates SNP genotype compression and exploits multiple CPU cores and graphics processing units (GPUs). This allows statistical geneticists to leverage commodity desktop computers in GWAS analysis and to avoid supercomputing.

AVAILABILITY

Source code is freely available at https://github.com/klkeys/IHT.jl.

Sensitized mutagenesis screen in Factor V Leiden mice identifies thrombosis suppressor loci

Factor V Leiden (F5L ) is a common genetic risk factor for venous thromboembolism in humans. We conducted a sensitized N-ethyl-N-nitrosourea (ENU) mutagenesis screen for dominant thrombosuppressor genes based on perinatal lethal thrombosis in mice homozygous for F5L (F5L/L ) and haploinsufficient for tissue factor pathway inhibitor (Tfpi+/- ). F8 deficiency enhanced the survival of F5L/LTfpi+/- mice, demonstrating that F5L/LTfpi+/- lethality is genetically suppressible. ENU-mutagenized F5L/L males and F5L/+Tfpi+/- females were crossed to generate 6,729 progeny, with 98 F5L/LTfpi+/- offspring surviving until weaning. Sixteen lines, referred to as "modifier of Factor 5 Leiden (MF5L1-16)," exhibited transmission of a putative thrombosuppressor to subsequent generations. Linkage analysis in MF5L6 identified a chromosome 3 locus containing the tissue factor gene (F3). Although no ENU-induced F3 mutation was identified, haploinsufficiency for F3 (F3+/- ) suppressed F5L/LTfpi+/- lethality. Whole-exome sequencing in MF5L12 identified an Actr2 gene point mutation (p.R258G) as the sole candidate. Inheritance of this variant is associated with suppression of F5L/LTfpi+/- lethality (P = 1.7 × 10-6), suggesting that Actr2p.R258G is thrombosuppressive. CRISPR/Cas9 experiments to generate an independent Actr2 knockin/knockout demonstrated that Actr2 haploinsufficiency is lethal, supporting a hypomorphic or gain-of-function mechanism of action for Actr2p.R258G Our findings identify F8 and the Tfpi/F3 axis as key regulators in determining thrombosis balance in the setting of F5L and also suggest a role for Actr2 in this process.

Family-specific aggregation of lipid GWAS variants confers the susceptibility to familial hypercholesterolemia in a large Austrian family

BACKGROUND AND AIMS

Hypercholesterolemia confers susceptibility to cardiovascular disease (CVD). Both serum total cholesterol (TC) and LDL-cholesterol (LDL-C) exhibit a strong genetic component (heritability estimates 0.41-0.50). However, a large part of this heritability cannot be explained by the variants identified in recent extensive genome-wide association studies (GWAS) on lipids. Our aim was to find genetic causes leading to high LDL-C levels and ultimately CVD in a large Austrian family presenting with what appears to be autosomal dominant inheritance for familial hypercholesterolemia (FH).

METHODS

We utilized linkage analysis followed by whole-exome sequencing and genetic risk score analysis using an Austrian multi-generational family with various dyslipidemias, including elevated TC and LDL-C, and one family branch with elevated lipoprotein (a) (Lp(a)).

RESULTS

We did not find evidence for genome-wide significant linkage for LDL-C or apparent causative variants in the known FH genes rather, we discovered a particular family-specific combination of nine GWAS LDL-C SNPs (p = 0.02 by permutation), and putative less severe familial hypercholesterolemia mutations in the LDLR and APOB genes in a subset of the affected family members. Separately, high Lp(a) levels observed in one branch of the family were explained primarily by the LPA locus, including short (<23) Kringle IV repeats and rs3798220.

CONCLUSIONS

Taken together, some forms of FH may be explained by family-specific combinations of LDL-C GWAS SNPs.

Prevalence and Penetrance of BRCA1 and BRCA2 Germline Mutations in Colombian Breast Cancer Patients

Pathogenic BRCA1/2 germline mutations confer high risks of breast and ovarian cancer to women of European ancestry. Characterization of BRCA1/2 mutations in other ethnic groups is also medically important. We comprehensively screened 68 Colombian breast/ovarian cancer families for small-range mutations, 221 families for large-genomic rearrangements, and 1,022 unselected breast cancer cases for Colombian founder mutations in BRCA1/2. The risk of cancer among relatives of mutation carriers and the mutation penetrance were estimated by survival analysis. Identified BRCA2 mutations included 6310delGA and the recurrent 1991del4 mutations. A novel large BRCA2 deletion was found in 0.9% of the screened families. Among unselected breast cancer cases, 3.3% tested positive for BRCA1/3450del4, 2.2% for BRCA1/A1708E, 1.1% for BRCA2/3034del4, and 0.4% for BRCA2/1991del4. Female relatives of carriers of BRCA1/2 founder mutations showed a 5.90 times higher risk of breast cancer, when the woman herself carried a BRCA1 mutation compared to a non-carrier (95% CI 2.01–17.3). The estimated cumulative risk of breast cancer by age 70 years for BRCA1 mutations carriers was 14% (95% CI 5–38) compared to 3% for the general Colombian population (relative risk of breast cancer 4.05). Together with known founder mutations, reported novel variants may ease a cost-effective BRCA1/2 screening in women with Colombian ancestry.

Subtypes of Native American ancestry and leading causes of death: Mapuche ancestry-specific associations with gallbladder cancer risk in Chile

Latin Americans are highly heterogeneous regarding the type of Native American ancestry. Consideration of specific associations with common diseases may lead to substantial advances in unraveling of disease etiology and disease prevention.

Here we investigate possible associations between the type of Native American ancestry and leading causes of death. After an aggregate-data study based on genome-wide genotype data from 1805 admixed Chileans and 639,789 deaths, we validate an identified association with gallbladder cancer relying on individual data from 64 gallbladder cancer patients, with and without a family history, and 170 healthy controls. Native American proportions were markedly underestimated when the two main types of Native American ancestry in Chile, originated from the Mapuche and Aymara indigenous peoples, were combined together. Consideration of the type of Native American ancestry was crucial to identify disease associations. Native American ancestry showed no association with gallbladder cancer mortality (P = 0.26). By contrast, each 1% increase in the Mapuche proportion represented a 3.7% increased mortality risk by gallbladder cancer (95%CI 3.1–4.3%, P = 6×10⁻²⁷). Individual-data results and extensive sensitivity analyses confirmed the association between Mapuche ancestry and gallbladder cancer. Increasing Mapuche proportions were also associated with an increased mortality due to asthma and, interestingly, with a decreased mortality by diabetes. The mortality due to skin, bladder, larynx, bronchus and lung cancers increased with increasing Aymara proportions. Described methods should be considered in future studies on human population genetics and human health. Complementary individual-based studies are needed to apportion the genetic and non-genetic components of associations identified relying on aggregate-data.

A lot of attention has been paid to Latino heterogeneity related to individual proportions of Native American, European and African ancestry. The importance of the type of Native American ancestry for health, however, has hardly been studied. Here we examined genetic data from 2,039 admixed Chileans to investigate possible associations between top causes of death and the two major types of Native American ancestry in Chile. Our findings demonstrate the necessity of suitable surrogates for ancestry estimation which mirror the actual composition of the study population, and the advantage of considering fine-scale Latino heterogeneity for unraveling of disease etiology and personalized healthcare.

Using an Event-History with Risk-Free Model to Study the Genetics of Alcoholism

Case–control genetic association studies typically ignore possible later disease onset in currently healthy subjects and assume that subjects with diseases equally contribute to the likelihood for inference, regardless of their onset age. Therefore, we used an event-history with risk-free model to simultaneously characterize alcoholism susceptibility and onset age in 65 independent non-Hispanic Caucasian males in the Collaborative Study on the Genetics of Alcoholism. Following data quality control, we analysed 22 single nucleotide polymorphisms (SNPs) on 12 candidate genes. The single-SNP analysis showed that the dominant minor allele of rs2134655 on DRD3 increases alcoholism susceptibility; the dominant minor allele of rs1439047 on NTRK2 delays the alcoholism onset age, but the additive minor allele of rs172677 on GRIN2B and the dominant minor allele of rs63319 on ALDH1A1 advance the alcoholism onset age; and the dominant minor allele of rs1079597 on DRD2 shortens the onset age range. Similarly, multiple-SNPs analysis revealed joint effects of rs2134655, rs172677 and rs1079597, with an adjustment for habitual smoking. This study provides a more comprehensive understanding of the genetics of alcoholism than previous case–control studies.

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel.

Generalized myoclonic epilepsy with photosensitivity in juvenile dogs caused by a defective DIRAS family GTPase 1

The clinical and electroencephalographic features of a canine generalized myoclonic epilepsy with photosensitivity and onset in young Rhodesian Ridgeback dogs (6 wk to 18 mo) are described. A fully penetrant recessive 4-bp deletion was identified in the DIRAS family GTPase 1 (DIRAS1) gene with an altered expression pattern of DIRAS1 protein in the affected brain. This neuronal DIRAS1 gene with a proposed role in cholinergic transmission provides not only a candidate for human myoclonic epilepsy but also insights into the disease etiology, while establishing a spontaneous model for future intervention studies and functional characterization.

Human Birth Weight and Reproductive Immunology: Testing for Interactions between Maternal and Offspring KIR and HLA-C Genes

BACKGROUND/AIMS

Maternal and offspring cell contact at the site of placentation presents a plausible setting for maternal-fetal genotype (MFG) interactions affecting fetal growth. We test hypotheses regarding killer cell immunoglobulin-like receptor (KIR) and HLA-C MFG effects on human birth weight by extending the quantitative MFG (QMFG) test.

METHODS

Until recently, association testing for MFG interactions had limited applications. To improve the ability to test for these interactions, we developed the extended QMFG test, a linear mixed-effect model that can use multi-locus genotype data from families.

RESULTS

We demonstrate the extended QMFG test's statistical properties. We also show that if an offspring-only model is fit when MFG effects exist, associations can be missed or misattributed. Furthermore, imprecisely modeling the effects of both KIR and HLA-C could result in a failure to replicate if these loci's allele frequencies differ among populations. To further illustrate the extended QMFG test's advantages, we apply the extended QMFG test to a UK cohort study and the Norwegian Mother and Child Cohort (MoBa) study.

CONCLUSION

We find a significant KIR-HLA-C interaction effect on birth weight. More generally, the QMFG test can detect genetic associations that may be missed by standard genome-wide association studies for quantitative traits.

Genetic Susceptibility in Acute Pancreatitis: Genotyping of GSTM1, GSTT1, GSTP1, CASP7, CASP8, CASP9, CASP10, LTA, TNFRSF1B, and TP53 Gene Variants

OBJECTIVES

Genetic testing could play a critical role in diagnosis and prognosis of acute pancreatitis (AP) and guide effective therapeutic interventions. We hypothesized that genetic polymorphisms in apoptosis and oxidative stress genes could determine incidence or severity in AP.

METHODS

We conducted a hospital-based case-control study in a white Portuguese population (133 AP patients and 232 age- and sex-matched healthy controls) to evaluate the role of 15 gene polymorphisms (2 deletions and 13 single nucleotide polymorphisms [SNPs]) in oxidative stress (GSTM1, GSTT1, GSTP1) and apoptosis genes (CASP7, CASP8, CASP9, CASP10, LTA, TNFRSF1B, TP53) in AP. Criteria for AP were abdominal pain, hyperamylasemia, and contrast-enhanced computed tomography.

RESULTS

The presence of GSTM1 is associated with increased susceptibility for AP, and the GSTP1 Val105Ile SNP is associated with an increased risk for AP in men. CASP9 Phe136Leu/Phe136Phe SNPs (heterozygotes) increases the risk for mild AP (odds ratio, 3.616; 95% confidence interval, 1.151-11.364; P < 0.05), whereas the homozygotic genotype of CASP9 Ala28Val decreases risk for mild AP (odds ratio, 0.296; 95% confidence interval, 0.091-0.963; P < 0.05).

CONCLUSIONS

Our results suggest that variations in GSTM1, GSTP1, and CASP9 may influence risk for AP.

Meta-Analysis for Penalized Regression Methods with Multi-Cohort Genome-Wide Association Studies

OBJECTIVE

Penalized regression has been successfully applied in genome-wide association studies. While meta-analysis is often conducted to increase power and protect patients' confidentiality, methods for meta-analyzing results of penalized regression in multi-cohort setting are still under development.

METHODS

We propose to use a data-splitting method to obtain valid p values (or equivalently, coefficient estimates and standard errors) for meta-analysis across multiple cohorts. We examine two ways of splitting data in multi-cohort setting and propose three methods to conduct meta-analysis based on p values. We compare the three meta-analysis methods to mega-analysis, which consists of pooling individual level data. We also apply our proposed meta-analysis approaches to the Framingham Heart Study data, where we divide the original dataset into four parts to create a multi-cohort scenario.

RESULTS

The simulations suggest that splitting cohorts has better performance than splitting data within each cohort. The real data application also shows that this method provides results that are similar to the mega-analysis.

CONCLUSION

After comparing the three methods that we proposed to conduct meta-analysis, we recommend splitting cohorts rather than datasets to obtain valid p values for meta-analysis of results from penalized regression in multi-cohort setting.

Genome-wide QTL and eQTL analyses using Mendel

Pedigree genome-wide association studies (GWAS) (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large-scale genome-wide quantitative trait locus (QTL) analysis. This analysis (a) works for random sample data, pedigree data, or a mix of both; (b) is highly efficient in both run time and memory requirement; (c) accommodates both univariate and multivariate traits; (d) works for autosomal and x-linked loci; (e) correctly deals with missing data in traits, covariates, and genotypes; (f) allows for covariate adjustment and constraints among parameters; (g) uses either theoretical or single nucleotide polymorphism (SNP)–based empirical kinship matrix for additive polygenic effects; (h) allows extra variance components such as dominant polygenic effects and household effects; (i) detects and reports outlier individuals and pedigrees; and (j) allows for robust estimation via the t-distribution. This paper assesses these capabilities on the genetics analysis workshop 19 (GAW19) sequencing data. We analyzed simulated and real phenotypes for both family and random sample data sets. For instance, when jointly testing the 8 longitudinally measured systolic blood pressure and diastolic blood pressure traits, it takes Mendel 78 min on a standard laptop computer to read, quality check, and analyze a data set with 849 individuals and 8.3 million SNPs. Genome-wide expression QTL analysis of 20,643 expression traits on 641 individuals with 8.3 million SNPs takes 30 h using 20 parallel runs on a cluster. Mendel is freely available at http://www.genetics.ucla.edu/software.