Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes.

Family-based sequencing studies are increasingly used to find rare genetic variants of high risk for disease traits with familial clustering. In some studies, families with multiple disease subtypes are collected and the exomes of affected relatives are sequenced for shared rare variants (RVs). Since different families can harbor different causal variants and each family harbors many RVs, tests to detect causal variants can have low power in this study design. Our goal is rather to prioritize shared variants for further investigation by, for example, pathway analyses or functional studies. The transmission-disequilibrium test prioritizes variants based on departures from Mendelian transmission in parent-child trios. Extending this idea to families, we propose methods to prioritize RVs shared in affected relatives with two disease subtypes, with one subtype more heritable than the other. Global approaches condition on a variant being observed in the study and assume a known probability of carrying a causal variant. In contrast, local approaches condition on a variant being observed in specific families to eliminate the carrier probability. Our simulation results indicate that global approaches are robust to misspecification of the carrier probability and prioritize more effectively than local approaches even when the carrier probability is misspecified.

Methods and Software to Analyze Gene-Environment Interactions under a Case-Mother-Control-Mother Design with Partially Missing Child Genotype.

INTRODUCTION: The case-mother-control-mother design allows to study fetal and maternal genetic factors together with environmental exposures on early life outcomes. Mendelian constraints and conditional independence between child genotype and environmental factors enabled semiparametric likelihood methods to estimate logistic models with greater efficiency than standard logistic regression. Difficulties in child genotype collection require methods handling missing child genotype. METHODS: We review a stratified retrospective likelihood and two semiparametric likelihood approaches: a prospective one and a modified retrospective one, the latter either modeling the maternal genotype as a function of covariates or leaving their joint distribution unspecified (robust version). We also review software implementing these modeling alternatives, compare their statistical properties in a simulation study, and illustrate their application, focusing on gene-environment interactions and partially missing child genotype. RESULTS: The robust retrospective likelihood provides generally unbiased estimates, with standard errors only slightly larger than when modeling maternal genotype based on exposure. The prospective likelihood encounters maximization problems. In the application to the association of small-for-gestational-age babies with CYP2E1 and drinking water disinfection by-products, the retrospective likelihood allowed a full array of covariates, while the prospective likelihood was limited to few covariates. CONCLUSION: We recommend the robust version of the modified retrospective likelihood.

Efficient inference of parent-of-origin effect using case-control mother-child genotype data.

Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother-child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect through a logistic regression model, with predictors including maternal and child genotypes, parental origins, and covariates. The parental origins may not be fully inferred from genotypes of a target genetic marker, so we propose to use genotypes of markers tightly linked to the target marker to increase inference efficiency. A robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation-maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-likelihood. This algorithm differs from the conventional expectation-maximization algorithm in the sense that it is based on a modified instead of the original profile log-likelihood function. The convergence of the algorithm is established under some mild regularity conditions. This expectation-maximization algorithm also allows convenient handling of missing child genotypes. Large sample properties, including weak consistency, asymptotic normality, and asymptotic efficiency, are established for the proposed estimator under some mild regularity conditions. Finite sample properties are evaluated through extensive simulation studies and the application to a real dataset.

Heterogeneity in the longitudinal courses of global functioning in children at familial risk of major psychiatric disorders: Association with trauma and familial characteristics.

OBJECTIVES: The extent to which heterogeneity in childhood risk trajectories may underlie later heterogeneity in schizophrenia (SZ), bipolar disorder (BP), and major depressive disorder (MDD) remains a chief question. Answers may optimally be found by studying the longitudinal trajectories of children born to an affected parent. We aimed to differentiate trajectories of global functioning and their sensitive periods from the age of 6 to 17 years in children at familial risk (FHRs). METHODS: First, a latent class mixed model analysis (LCMM) was applied to yearly ratings of the Children's Global Assessment Scale (CGAS) from the age of 6 to 17 years in 170 FHRs born to a parent affected by DSM-IV SZ (N = 37), BP (N = 82) or MDD (N = 51). Then, we compared the obtained Classes or trajectories of FHRs in terms of sex, parental diagnosis, IQ, child clinical status, childhood trauma, polygenic risk score (PRS), and outcome in transition to illness. RESULTS: The LCMM on yearly CGAS trajectories identified a 4-class solution showing markedly different childhood and adolescence dynamic courses and temporal vulnerability windows marked by a functioning decline and a degree of specificity in parental diagnosis. Moreover, IQ, trauma exposure, PRS level, and timing of later transition to illness differentiated the trajectories. Almost half (46%) of the FHRs exhibited a good and stable global functioning trajectory. CONCLUSIONS: FHRs of major psychiatric disorders show heterogeneous functional decline during development associated with parental diagnosis, polygenic risk loading, and childhood trauma.

Time trends in social contacts before and during the COVID-19 pandemic: the CONNECT study.

BACKGROUND: Since the beginning of the COVID-19 pandemic, many countries, including Canada, have adopted unprecedented physical distancing measures such as closure of schools and non-essential businesses, and restrictions on gatherings and household visits. We described time trends in social contacts for the pre-pandemic and pandemic periods in Quebec, Canada. METHODS: CONNECT is a population-based study of social contacts conducted shortly before (2018/2019) and during the COVID-19 pandemic (April 2020 - February 2021), using the same methodology for both periods. We recruited participants by random digit dialing and collected data by self-administered web-based questionnaires. Questionnaires documented socio-demographic characteristics and social contacts for two assigned days. A contact was defined as a two-way conversation at a distance ≤ 2 m or as a physical contact, irrespective of masking. We used weighted generalized linear models with a Poisson distribution and robust variance (taking possible overdispersion into account) to compare the mean number of social contacts over time and by socio-demographic characteristics. RESULTS: A total of 1291 and 5516 Quebecers completed the study before and during the pandemic, respectively. Contacts significantly decreased from a mean of 8 contacts/day prior to the pandemic to 3 contacts/day during the spring 2020 lockdown. Contacts remained lower than the pre-COVID period thereafter (lowest = 3 contacts/day during the Christmas 2020/2021 holidays, highest = 5 in September 2020). Contacts at work, during leisure activities/in other locations, and at home with visitors showed the greatest decreases since the beginning of the pandemic. All sociodemographic subgroups showed significant decreases of contacts since the beginning of the pandemic. The mixing matrices illustrated the impact of public health measures (e.g. school closure, gathering restrictions) with fewer contacts between children/teenagers and fewer contacts outside of the three main diagonals of contacts between same-age partners/siblings and between children and their parents. CONCLUSION: Physical distancing measures in Quebec significantly decreased social contacts, which most likely mitigated the spread of COVID-19.

Exploring polypharmacy with artificial intelligence: data analysis protocol.

BACKGROUND: Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for traditional statistical methods to predict which therapy is genuinely associated with health outcomes. The project aims to use artificial intelligence (AI) to determine the quality of polypharmacy among older adults with chronic diseases in the province of Québec, Canada. METHODS: We will use data from the Quebec Integrated Chronic Disease Surveillance System (QICDSS). QICDSS contains information about prescribed medications in older adults in Quebec collected over 20 years. It also includes diagnostic codes and procedures, and sociodemographic data linked through a unique identification number for each individual. Our research will be structured around three interconnected research axes: AI, Health, and Law&Ethics. The AI research axis will develop algorithms for finding frequent patterns of medication use that correlate with health events, considering data locality and temporality (explainable AI or XAI). The Health research axis will translate these patterns into polypharmacy indicators relevant to public health surveillance and clinicians. The Law&Ethics axis will assess the social acceptability of the algorithms developed using AI tools and the indicators developed by the Heath axis and will ensure that the developed indicators neither discriminate against any population group nor increase the disparities already present in the use of medications. DISCUSSION: The multi-disciplinary research team consists of specialists in AI, health data, statistics, pharmacy, public health, law, and ethics, which will allow investigation of polypharmacy from different points of view and will contribute to a deeper understanding of the clinical, social, and ethical issues surrounding polypharmacy and its surveillance, as well as the use of AI for health record data. The project results will be disseminated to the scientific community, healthcare professionals, and public health decision-makers in peer-reviewed publications, scientific meetings, and reports. The diffusion of the results will ensure the confidentiality of individual data.

Inferring disease risk genes from sequencing data in multiplex pedigrees through sharing of rare variants.

We previously demonstrated how sharing of rare variants (RVs) in distant affected relatives can be used to identify variants causing a complex and heterogeneous disease. This approach tested whether single RVs were shared by all sequenced affected family members. However, as with other study designs, joint analysis of several RVs (e.g., within genes) is sometimes required to obtain sufficient statistical power. Further, phenocopies can lead to false negatives for some causal RVs if complete sharing among affected is required. Here, we extend our methodology (Rare Variant Sharing, RVS) to address these issues. Specifically, we introduce gene-based analyses, a partial sharing test based on RV sharing probabilities for subsets of affected relatives and a haplotype-based RV definition. RVS also has the desirable feature of not requiring external estimates of variant frequency or control samples, provides functionality to assess and address violations of key assumptions, and is available as open source software for genome-wide analysis. Simulations including phenocopies, based on the families of an oral cleft study, revealed the partial and complete sharing versions of RVS achieved similar statistical power compared with alternative methods (RareIBD and the Gene-Based Segregation Test), and had superior power compared with the pedigree Variant Annotation, Analysis, and Search Tool (pVAAST) linkage statistic. In studies of multiplex cleft families, analysis of rare single nucleotide variants in the exome of 151 affected relatives from 54 families revealed no significant excess sharing in any one gene, but highlighted different patterns of sharing revealed by the complete and partial sharing tests.

Detection of rare disease variants in extended pedigrees using RVS.

SUMMARY: Family-based sequencing studies enable researchers to identify highly penetrant genetic variants too rare to be tested in conventional case-control studies, by studying co-segregation of variant and disease phenotypes. When multiple affected subjects in a family are sequenced, the probability that a variant or a set of variants is shared identical-by-descent by some or all affected relatives provides evidence against the null hypothesis of complete absence of linkage and association. The Rare Variant Sharing software package RVS implements a suite of tools to assess association and linkage between rare genetic variants and a dichotomous disease indicator in family pedigrees. AVAILABILITY AND IMPLEMENTATION: RVS is available as open source software from the Bioconductor webpage at https://bioconductor.org/packages/release/bioc/html/RVS.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exposure to polychlorinated biphenyls and organochlorine pesticides and risk of dementia, Alzheimer's disease and cognitive decline in an older population: a prospective analysis from the Canadian Study of Health and Aging.

BACKGROUND: Little attention has been paid to neurotoxicants on the risk of dementia. Exposure to known neurotoxicants such as polychlorinated biphenyls (PCBs) and organochlorine (OC) pesticides is suspected to have adverse cognitive effects in older populations. OBJECTIVE: To assess whether plasma concentrations of PCBs and OC pesticides are associated with the risk of cognitive decline, Alzheimer's disease (AD) and of all-cause dementia in the Canadian older population. METHODS: Analyses were based on data from the Canadian Study of Health and Aging, a 3-phase, 10-year population-based study of individuals aged 65+ years. Analyses included 669 clinically assessed subjects, of which 156 developed dementia including 108 incident cases of AD. Subjects were screened at each phase with the 100-point Modified Mini-Mental State Examination (3MS), a measurement of global cognitive function. Statistical analyses included Cox proportional hazards model when the outcome was dementia or AD, and a repeated-measure mixed model when the outcome was the 3MS score. RESULTS: No association of PCB and OC pesticides with the risk of dementia and AD was observed. Elevated concentrations of PCB congeners nos 118, 153, 156, 163, and OC pesticides 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (p,p'-DDT) and its metabolite 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (p,p'-DDE) were significantly associated with cognitive decline as assessed with the 3MS. A posteriori analyses suggested that only p,p'-DDE was significantly related to a higher cognitive decline in time based on the 3MS among incident cases of dementia compared to subjects remaining nondemented. CONCLUSION: PCB and OC pesticide plasma concentrations were not related to the incident diagnosis of neither dementia, nor AD. Using the 3MS scores as the outcome, higher concentrations of four PCB congeners and two OC pesticides were associated with lower cognitive performances in subjects. The association of p,p'-DDE with cognitive decline in time in incident cases of dementia merits further investigation.

Whole exome association of rare deletions in multiplex oral cleft families.

By sequencing the exomes of distantly related individuals in multiplex families, rare mutational and structural changes to coding DNA can be characterized and their relationship to disease risk can be assessed. Recently, several rare single nucleotide variants (SNVs) were associated with an increased risk of nonsyndromic oral cleft, highlighting the importance of rare sequence variants in oral clefts and illustrating the strength of family-based study designs. However, the extent to which rare deletions in coding regions of the genome occur and contribute to risk of nonsyndromic clefts is not well understood. To identify putative structural variants underlying risk, we developed a pipeline for rare hemizygous deletions in families from whole exome sequencing and statistical inference based on rare variant sharing. Among 56 multiplex families with 115 individuals, we identified 53 regions with one or more rare hemizygous deletions. We found 45 of the 53 regions contained rare deletions occurring in only one family member. Members of the same family shared a rare deletion in only eight regions. We also devised a scalable global test for enrichment of shared rare deletions.

Polyunphased: an extension to polytomous outcomes of the Unphased package for family-based genetic association analysis.

Polytomous phenotypes arise when a disease has multiple subtypes or when two dichotomous phenotypes are analyzed simultaneously. Few software programs offer the option to analyze such phenotypes in family studies, and none implements conditional polytomous logistic regression for within-family analysis robust to population stratification. We introduce Polyunphased, an extension to polytomous phenotypes of the Unphased package, a flexible software tool for genetic association analysis in nuclear families. Like Unphased, Polyunphased is written in C++ and runs from the command line or from a Java graphical user interface. Most Unphased options remain available in Polyunphased, including those handling missing parental genotypes while preserving robustness to population stratification, and the modelling options. Simulation studies confirmed the expected statistical behaviour of the maximum likelihood estimates of the association parameters of the conditional logistic regression model when the corresponding association parameters in the parental term of the likelihood function are set to 0, but revealed convergence problems when estimating these parental association parameters separately. The former approach is thus recommended with polytomous phenotypes.

Polygenic risk scores distinguish patients from non-affected adult relatives and from normal controls in schizophrenia and bipolar disorder multi-affected kindreds.

Recent studies have used results on SNP association with schizophrenia (SZ) and bipolar disorder (BD) to create polygenic risk scores (PRS) discriminating non-familial unrelated patients from controls. Little is known about the role of PRS in densely affected multigenerational families. We tested PRS differences between affected SZ and BD family members from their non-affected adult relatives (NAARs) in Eastern Quebec Kindreds and from controls. We examined 1227 subjects: from 17 SZ and BD kindreds, we studied 153 patients (57 SZ, 13 schizoaffective, and 83 BD) and 180 NAARs, and 894 unrelated controls from the Eastern Quebec population. PRS were derived from published case-control association studies of SZ and BD. We also constructed a combined SZ and BD PRS by using SNPs from both SZ and BD PRS. SZ patients had higher SZ PRS than controls (p = 0.0039, R2 = 0.027) and BD patients had higher BD PRS than controls (p = 0.013, R2 = 0.027). Differences between affected subjects and NAARs and controls were significant with both SZ and BD PRS. Moreover, a combined SZ-BD PRS was also significantly associated with SZ and BD when compared to NAARs (p = 0.0019, R2 = 0.010) and controls (p = 0.0025, R2 = 0.028), revealing a SZ-BD commonality effect in PRS at the diagnosis level. The SZ and the BD PRS, however, showed a degree of specificity regarding thought disorder symptoms. Overall, our report would confirm the usefulness of PRS in capturing the contribution of common genetic variants to the risk of SZ and BD in densely affected families.

Reduced Antibody Response to Infant Measles Vaccination: Effects Based on Type and Timing of the First Vaccine Dose Persist After the Second Dose.

Background: The effect of age at first dose on the immunogenicity of a 2-dose pediatric schedule of measles-mumps-rubella (MMR) or measles-mumps-rubella-varicella (MMRV) vaccine was assessed in children born to mostly vaccinated mothers. Methods: Immunogenicity data among children given their first measles vaccine dose between 11 and 22 months of age were pooled from 5 randomized controlled trials conducted in Europe and the United States between 2004 and 2010. Measles antibody titers were measured by enzyme-linked immunosorbent assay before and after each dose; geometric mean concentrations (GMCs) and the proportion seronegative (GMC <150 mIU/mL) were derived by age at first dose. Results: Among 5542 children given a first measles vaccine dose at 11, 12, 13-14, and 15-22 months of age, the proportion seronegative decreased from 8.5% to 3.2%, 2.4%, and 1.5%, respectively (P < .001), whereas GMCs increased with older age measles vaccine initiation (P < .001). MMRV induced higher GMCs than MMR (P < .001). First and second dose GMCs were highly correlated (Spearman coefficient = 0.8). Conclusions: As previously noted among infants born to mothers with history of wild-type measles, antibody responses among children born to vaccinated mothers were reduced based on earlier administration of their first measles vaccine dose at ≤12 vs ≥15 months of age. Negative effects of earlier age at first measles vaccine dose persisted after the second dose. The measles elimination goal may require a careful balance between earlier infant protection and the risk of reduced antibody responses and secondary vaccine failure among successive birth cohorts systematically initiated to measles vaccination <15 months of age.

When Is an Endophenotype Useful to Detect Association to a Disease? Exploring the Relationships between Disease Status, Endophenotype and Genetic Polymorphisms.

OBJECTIVES: To investigate the conditions and analysis strategies required so that endophenotypes related to a disease help discover genetic variants involved in the disease. METHODS: The association with disease susceptibility variants is examined as a function of the relationships between disease status, endophenotype values and the genotype at another disease or endophenotype susceptibility locus assumed to be previously known, using approximate linear models of allele frequencies as a function of these variables and simulations in the context of family studies when the endophenotype is dichotomous. RESULTS: Under genetic mechanisms where the risk allele of the tested locus has an effect exclusively in subjects with the endophenotype, the risk allele frequency differences between affected and unaffected subjects are much greater in the subset of subjects with an endophenotype impairment than in those without such an impairment, and power gains are obtained when testing the association under a joint disease-endophenotype model, both with two-locus or single-locus tests. However, with moderate main effect on the risk of disease or endophenotype impairment, testing directly the association between risk allele and disease or endophenotype is more powerful than testing under a joint disease-endophenotype model. CONCLUSIONS: Joint modeling of disease and endophenotype should be used only in parallel with standard disease association testing.

On the validity of within-nuclear-family genetic association analysis in samples of extended families.

Splitting extended families into their component nuclear families to apply a genetic association method designed for nuclear families is a widespread practice in familial genetic studies. Dependence among genotypes and phenotypes of nuclear families from the same extended family arises because of genetic linkage of the tested marker with a risk variant or because of familial specificity of genetic effects due to gene-environment interaction. This raises concerns about the validity of inference conducted under the assumption of independence of the nuclear families. We indeed prove theoretically that, in a conditional logistic regression analysis applicable to disease cases and their genotyped parents, the naive model-based estimator of the variance of the coefficient estimates underestimates the true variance. However, simulations with realistic effect sizes of risk variants and variation of this effect from family to family reveal that the underestimation is negligible. The simulations also show the greater efficiency of the model-based variance estimator compared to a robust empirical estimator. Our recommendation is therefore, to use the model-based estimator of variance for inference on effects of genetic variants.

Inferring rare disease risk variants based on exact probabilities of sharing by multiple affected relatives.

MOTIVATION: Family-based designs are regaining popularity for genomic sequencing studies because they provide a way to test cosegregation with disease of variants that are too rare in the population to be tested individually in a conventional case-control study. RESULTS: Where only a few affected subjects per family are sequenced, the probability that any variant would be shared by all affected relatives-given it occurred in any one family member-provides evidence against the null hypothesis of a complete absence of linkage and association. A P-value can be obtained as the sum of the probabilities of sharing events as (or more) extreme in one or more families. We generalize an existing closed-form expression for exact sharing probabilities to more than two relatives per family. When pedigree founders are related, we show that an approximation of sharing probabilities based on empirical estimates of kinship among founders obtained from genome-wide marker data is accurate for low levels of kinship. We also propose a more generally applicable approach based on Monte Carlo simulations. We applied this method to a study of 55 multiplex families with apparent non-syndromic forms of oral clefts from four distinct populations, with whole exome sequences available for two or three affected members per family. The rare single nucleotide variant rs149253049 in ADAMTS9 shared by affected relatives in three Indian families achieved significance after correcting for multiple comparisons ([Formula: see text]). AVAILABILITY AND IMPLEMENTATION: Source code and binaries of the R package RVsharing are freely available for download at http://cran.r-project.org/web/packages/RVsharing/index.html. CONTACT: alexandre.bureau@msp.ulaval.ca or ingo@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A genome-wide association study of suicidal behavior.

Genome wide array studies have reported limited success in identifying genetic markers conferring risk for suicidal behavior (SB). This may be attributable to study designs with primary outcome other than SB. We performed a GWAS on suicides and cases with a history of nonfatal suicide attempts compared with psychiatric controls and healthy volunteers. A consortium of USA, Canadian and German teams assembled two groups of cases (suicide attempters and suicides, N = 577) and non-attempter psychiatric and healthy controls (N = 1,233). Logistic regression was used to test genotype-suicidal behavior association. The test was repeated separating suicide attempt and completed suicide as outcomes. No SNP reached genome-wide significance, but several SNPs within STK3, ADAMTS14, PSME2, and TBX20 genes reached P < 1 × 10(-5) . The top SNPs for the suicide attempt analysis included two from DPP10, one from CTNNA3 and one from STK32B. In the suicide analysis we found seven SNPs from the TBX20 gene in the top hits. Pathway analysis identified the following pathways: "Cellular Assembly and Organization," "Nervous System Development and Function," "Cell Death and Survival," "Immunological Disease," "Infectious Disease," and "Inflammatory Response." The top genes in the SB analysis did not overlap with those in the ideation analysis. No genome wide significant results suggest that susceptibility to SB has genetic risk factors with smaller effect sizes. The strongest candidate genes, ADAMTS14, and PSME2 (both linked to inflammatory response), STK3 (neuronal cell death), and TBX20 (brainstem motor neuron development), have not been previously reported in association with suicide and warrant further study.

Detection of phenotype modifier genes using two-locus linkage analysis in complex disorders such as major psychosis.

OBJECTIVE: To increase power to detect modifier loci conferring susceptibility to specific phenotypes such as disease diagnoses which are part of a broader disorder spectrum by jointly modeling a modifier and a broad susceptibility gene and to identify modifier loci conferring specific susceptibility to schizophrenia (SZ) or to bipolar disorder (BP) using the approach. METHODS: We implemented a two-locus linkage analysis model where a gene 1 genotype increases the risk of a broad phenotype and a gene 2 genotype modifies the expression of gene 1 by conferring susceptibility to a specific phenotype. RESULTS: Compared to a single-locus analysis within the broad phenotype, the proposed approach had greater power to detect the modifier gene 2 (0.96 vs. 0.54 under a simulation scenario including heterogeneity). In a sample of 12 mixed SZ and BP Eastern Quebec kindreds, D8S1110 at 8p22 showed the strongest evidence of linkage to a gene determining a specific phenotype (SZ or BP) among subjects susceptible to major psychosis because of putative genes at 10p13 (D10S245, conditional maximized LOD (cMOD) = 4.20, p = 0.0003) and 3q21-q23 (D3S2418, cMOD = 4.09, p = 0.0005). CONCLUSION: The proposed strategy is useful to detect modifier loci conferring susceptibility to a specific phenotype within a broader phenotype.

Multivariate extension of penalized regression on summary statistics to construct polygenic risk scores for correlated traits.

Genetic correlations between human traits and disorders such as schizophrenia (SZ) and bipolar disorder (BD) diagnoses are well established. Improved prediction of individual traits has been obtained by combining predictors of multiple genetically correlated traits derived from summary statistics produced by genome-wide association studies, compared with single trait predictors. We extend this idea to penalized regression on summary statistics in Multivariate Lassosum, expressing regression coefficients for the multiple traits on single nucleotide polymorphisms (SNPs) as correlated random effects, similarly to multi-trait summary statistic best linear unbiased predictors (MT-SBLUPs). We also allow the SNP contributions to genetic covariance and heritability to depend on genomic annotations. We conducted simulations with two dichotomous traits having polygenic architecture similar to SZ and BD, using genotypes from 29,330 subjects from the CARTaGENE cohort. Multivariate Lassosum produced polygenic risk scores (PRSs) more strongly correlated with the true genetic risk predictor and had better discrimination power between affected and non-affected subjects than previously published sparse multi-trait (PANPRS) and univariate (Lassosum, sparse LDpred2, and the standard clumping and thresholding) methods in most simulation settings. Application of Multivariate Lassosum to predict SZ, BD, and related psychiatric traits in the Eastern Quebec SZ and BD kindred study revealed associations with every trait stronger than those obtained with univariate sparse PRSs, particularly when heritability and genetic covariance depended on genomic annotations. Multivariate Lassosum thus appears promising to improve prediction of genetically correlated traits with summary statistics for a selected subset of SNPs.

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

With the cost-effectiveness technology in whole-genome sequencing, more sophisticated statistical methods for testing genetic association with both rare and common variants are being investigated to identify the genetic variation between individuals. Several methods which group variants, also called gene-based approaches, are developed. For instance, advanced extensions of the sequence kernel association test, which is a widely used variant-set test, have been proposed for unrelated samples and extended for family data. Family data have been shown to be powerful when analyzing rare variants. However, most of such methods capture familial relatedness using a random effect component within the generalized linear mixed model framework. Therefore, there is a need to develop unified and flexible methods to study the association between a set of genetic variants and a trait, especially for a binary outcome. Copulas are multivariate distribution functions with uniform margins on the [0,1] interval and they provide suitable models to capture familial dependence structure. In this work, we propose a flexible family-based association test for both rare and common variants in the presence of binary traits. The method, termed novel rare variant association test (NRVAT), uses a marginal logistic model and a Gaussian Copula. The latter is employed to model the dependence between relatives. An analytic score-type test is derived. Through simulations, we show that our method can achieve greater power than existing approaches. The proposed model is applied to investigate the association between schizophrenia and bipolar disorder in a family-based cohort consisting of 17 extended families from Eastern Quebec.