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Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained

Genome-wide association studies (GWASs) of Huntington disease (HD) have identified six DNA maintenance gene loci (among others) as modifiers and implicated a two step-mechanism of pathogenesis: somatic instability of the causative HTT CAG repeat with subsequent triggering of neuronal damage. The largest studies have been limited to HD individuals with a rater-estimated age at motor onset. To capitalize on the wealth of phenotypic data in several large HD natural history studies, we have performed algorithmic prediction by using common motor and cognitive measures to predict age at other disease landmarks as additional phenotypes for GWASs. Combined with imputation with the Trans-Omics for Precision Medicine reference panel, predictions using integrated measures provided objective landmark phenotypes with greater power to detect most modifier loci. Importantly, substantial differences in the relative modifier signal across loci, highlighted by comparing common modifiers at MSH3 and FAN1, revealed that individual modifier effects can act preferentially in the motor or cognitive domains. Individual components of the DNA maintenance modifier mechanisms may therefore act differentially on the neuronal circuits underlying the corresponding clinical measures. In addition, we identified additional modifier effects at the PMS1 and PMS2 loci and implicated a potential second locus on chromosome 7. These findings indicate that broadened discovery and characterization of HD genetic modifiers based on additional quantitative or qualitative phenotypes offers not only the promise of in-human validated therapeutic targets but also a route to dissecting the mechanisms and cell types involved in both the somatic instability and toxicity components of HD pathogenesis., Competing Interests: Declaration of interests J.M.L. serves on the scientific advisory board of GenEdit Inc. V.C.W. is a scientific advisory board member of Triplet Therapeutics Inc., a company developing new therapeutic approaches to address triplet repeat disorders such as Huntington disease and myotonic dystrophy. Her financial interests in Triplet Therapeutics were reviewed and are managed by Massachusetts General Hospital and Mass General Brigham in accordance with their conflict of interest policies. She is a scientific advisory board member of LoQus23 Therapeutics Ltd and has provided paid consulting services to Acadia Pharmaceuticals Inc., Alnylam Inc., and Biogen Inc. She has also received research support from Pfizer Inc. J.A.M. is a paid consultant for PTC Therapeutics Inc. and Behavioral Diagnostics Inc. and has also been a consultant for Wave Life Sciences USA Inc. T.H.M. is an associate member of the scientific advisory board of LoQus23 Therapeutics Ltd. G.B.L. has provided consulting services, advisory board functions, clinical trial services, and lectures for Acadia, Affiris, Allergan, Alnylam, Amarin, AOP Orphan Pharmaceuticals AG, Bayer Pharma AG, Boehringer-Ingelheim, CHDI Foundation, GlaxoSmithKline, Hoffmann-LaRoche, Ipsen, ISIS (Ionis) Pharma, Lundbeck, Neurosearch Inc., Medesis, Medivation, Medtronic, NeuraMetrix, Novartis, Pfizer, Prana Biotechnology, PTC Therapeutics, Raptor, Remix, Sangamo/Shire, Sanofi-Aventis, Siena Biotech, Takeda, Temmler Pharma GmbH, Teva Pharmaceuticals and Triplet Therapeutics. J.S.P. is a paid consultant for Acadia Pharmaceuticals and Wave Life Sciences USA Inc. E.R.D. has received research support from Wave Life Sciences USA Inc. D.G.M. has been a scientific consultant and/or received honoraria/stock options from AMO Pharma, Charles River, LoQus23, Small Molecule RNA, Triplet Therapeutics, and Vertex Pharmaceuticals and held research contracts with AMO Pharma and Vertex Pharmaceuticals within the last 5 years. L.J. is a member of the scientific advisory boards of LoQus23 Therapeutics Ltd and Triplet Therapeutics Inc. and a member of the executive committee of the European Huntington Disease Network. J.D.L. is a paid advisory board member for F. Hoffmann-La Roche Ltd and UniQure, and he is a paid consultant for Triplet Therapeutics, PTC Therapeutics, and Remix Therapeutics. J.F.G. is a scientific advisory board member and has a financial interest in Triplet Therapeutics Inc. His NIH-funded project is using genetic and genomic approaches to uncover other genes that significantly influence when diagnosable symptoms emerge and how rapidly they worsen in Huntington disease. The company is developing new therapeutic approaches to address triplet repeat disorders such Huntington disease, myotonic dystrophy, and spinocerebellar ataxias. His interests were reviewed and are managed by Massachusetts General Hospital and Mass General Brigham in accordance with their conflict of interest policies. J.F.G. has also been a consultant for Wave Life Sciences USA Inc., Biogen Inc., and Pfizer Inc., (Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

A recent genome-wide association study of Huntington disease (HD) implicated genes involved in DNA maintenance processes as modifiers of onset, including multiple genome-wide significant signals in a chr15 region containing the DNA repair gene Fanconi-Associated Nuclease 1 (FAN1). Here, we have carried out detailed genetic, molecular, and cellular investigation of the modifiers at this locus. We find that missense changes within or near the DNA-binding domain (p.Arg507His and p.Arg377Trp) reduce FAN1's DNA-binding activity and its capacity to rescue mitomycin C-induced cytotoxicity, accounting for two infrequent onset-hastening modifier signals. We also idenified a third onset-hastening modifier signal whose mechanism of action remains uncertain but does not involve an amino acid change in FAN1. We present additional evidence that a frequent onset-delaying modifier signal does not alter FAN1 coding sequence but is associated with increased FAN1 mRNA expression in the cerebral cortex. Consistent with these findings and other cellular overexpression and/or suppression studies, knockout of FAN1 increased CAG repeat expansion in HD-induced pluripotent stem cells. Together, these studies support the process of somatic CAG repeat expansion as a therapeutic target in HD, and they clearly indicate that multiple genetic variations act by different means through FAN1 to influence HD onset in a manner that is largely additive, except in the rare circumstance that two onset-hastening alleles are present. Thus, an individual's particular combination of FAN1 haplotypes may influence their suitability for HD clinical trials, particularly if the therapeutic agent aims to reduce CAG repeat instability., (Copyright © 2020 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Huntington disease (HD) is caused by an expanded HTT CAG repeat that leads in a length-dependent, completely dominant manner to onset of a characteristic movement disorder. HD also displays early mortality, so we tested whether the expanded CAG repeat exerts a dominant influence on age at death and on the duration of clinical disease. We found that, as with clinical onset, HD age at death is determined by expanded CAG-repeat length and has no contribution from the normal CAG allele. Surprisingly, disease duration is independent of the mutation's length. It is also unaffected by a strong genetic modifier of HD motor onset. These findings suggest two parsimonious alternatives. (1) HD pathogenesis is driven by mutant huntingtin, but before or near motor onset, sufficient CAG-driven damage occurs to permit CAG-independent processes and then lead to eventual death. In this scenario, some pathological changes and their clinical correlates could still worsen in a CAG-driven manner after disease onset, but these CAG-related progressive changes do not themselves determine duration. Alternatively, (2) HD pathogenesis is driven by mutant huntingtin acting in a CAG-dependent manner with different time courses in multiple cell types, and the cellular targets that lead to motor onset and death are different and independent. In this scenario, processes driven by HTT CAG length lead directly to death but not via the striatal pathology associated with motor manifestations. Each scenario has important ramifications for the design and testing of potential therapeutics, especially those aimed at preventing or delaying characteristic motor manifestations., (Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

We present a method for testing overrepresentation of biological pathways, indexed by gene-ontology terms, in lists of significant SNPs from genome-wide association studies. This method corrects for linkage disequilibrium between SNPs, variable gene size, and multiple testing of nonindependent pathways. The method was applied to the Wellcome Trust Case-Control Consortium Crohn disease (CD) data set. At a general level, the biological basis of CD is relatively well known for a complex genetic trait, and it thus acted as a test of the method. The method, known as ALIGATOR (Association LIst Go AnnoTatOR), successfully detected biological pathways implicated in CD. The method was also applied to a meta-analysis of bipolar disorder, and it implicated the modulation of transcription and cellular activity, including that which occurs via hormonal action, as an important player in pathogenesis.

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

Strong evidence of linkage to late-onset Alzheimer disease (LOAD) has been observed on chromosome 10, which implicates a wide region and at least one disease-susceptibility locus. Although significant associations with several biological candidate genes on chromosome 10 have been reported, these findings have not been consistently replicated, and they remain controversial. We performed a chromosome 10-specific association study with 1,412 gene-based single-nucleotide polymorphisms (SNPs), to identify susceptibility genes for developing LOAD. The scan included SNPs in 677 of 1,270 known or predicted genes; each gene contained one or more markers, about half (48%) of which represented putative functional mutations. In general, the initial testing was performed in a white case-control sample from the St. Louis area, with 419 LOAD cases and 377 age-matched controls. Markers that showed significant association in the exploratory analysis were followed up in several other white case-control sample sets to confirm the initial association. Of the 1,397 markers tested in the exploratory sample, 69 reached significance (P < .05). Five of these markers replicated at P < .05 in the validation sample sets. One marker, rs498055, located in a gene homologous to RPS3A (LOC439999), was significantly associated with Alzheimer disease in four of six case-control series, with an allelic P value of .0001 for a meta-analysis of all six samples. One of the case-control samples with significant association to rs498055 was derived from the linkage sample (P = .0165). These results indicate that variants in the RPS3A homologue are associated with LOAD and implicate this gene, adjacent genes, or other functional variants (e.g., noncoding RNAs) in the pathogenesis of this disorder.

Linkage between developmental dyslexia (DD) and chromosome 6p has been replicated in a number of independent samples. Recent attempts to identify the gene responsible for the linkage have produced inconsistent evidence for association of DD with a number of genes in a 575-kb region of chromosome 6p22.2, including VMP, DCDC2, KIAA0319, TTRAP, and THEM2. We aimed to identify the specific gene or genes involved by performing a systematic, high-density (approximately 2-3-kb intervals) linkage disequilibrium screen of these genes in an independent sample, incorporating family-based and case-control designs in which dyslexia was defined as an extreme representation of reading disability. Using DNA pooling, we first observed evidence for association with 17 single-nucleotide polymorphisms (SNPs), 13 of which were located in the KIAA0319 gene (P<.01-.003). After redundant SNPs were excluded, 10 SNPs were individually genotyped in 223 subjects with DD and 273 controls. Those SNPs that were significant at P

A genome scan was performed on the first phase sample of the Genetics of Recurrent Early-Onset Depression (GenRED) project. The sample consisted of 297 informative families containing 415 independent affected sibling pairs (ASPs), or, counting all possible pairs, 685 informative affected relative pairs (555 ASPs and 130 other pair types). Affected cases had recurrent major depressive disorder (MDD) with onset before age 31 years for probands or age 41 years for other affected relatives; the mean age at onset was 18.5 years, and the mean number of depressive episodes was 7.3. The Center for Inherited Disease Research genotyped 389 microsatellite markers (mean spacing of 9.3 cM). The primary linkage analysis considered allele sharing in all possible affected relative pairs with the use of the Z(lr) statistic computed by the ALLEGRO program. A secondary logistic regression analysis considered the effect of the sex of the pair as a covariate. Genomewide significant linkage was observed on chromosome 15q25.3-26.2 (Zlr=4.14, equivalent LOD = 3.73, empirical genomewide P=.023). The linkage was not sex specific. No other suggestive or significant results were observed in the primary analysis. The secondary analysis produced three regions of suggestive linkage, but these results should be interpreted cautiously because they depended primarily on the small subsample of 42 male-male pairs. Chromosome 15q25.3-26.2 deserves further study as a candidate region for susceptibility to MDD.

Schizophrenia candidate regions 33-51 cM in length on chromosomes 5q, 6q, 10p, and 13q were investigated for genetic linkage with mapped markers with an average spacing of 5.64 cM. We studied 734 informative multiplex pedigrees (824 independent affected sibling pairs [ASPs], or 1,003 ASPs when all possible pairs are counted), which were collected in eight centers. Cases with diagnoses of schizophrenia or schizoaffective disorder (DSM-IIIR criteria) were considered affected (n=1,937). Data were analyzed with multipoint methods, including nonparametric linkage (NPL), ASP analysis using the possible-triangle method, and logistic-regression analysis of identity-by-descent (IBD) sharing in ASPs with sample as a covariate, in a test for intersample heterogeneity and for linkage with allowance for intersample heterogeneity. The data most supportive for linkage to schizophrenia were from chromosome 6q; logistic-regression analysis of linkage allowing for intersample heterogeneity produced an empirical P value <.0002 with, or P=.0004 without, inclusion of the sample that produced the first positive report in this region; the maximum NPL score in this region was 2.47 (P=.0046), the maximum LOD score (MLS) from ASP analysis was 3.10 (empirical P=.0036), and there was significant evidence for intersample heterogeneity (empirical P=.0038). More-modest support for linkage was observed for chromosome 10p, with logistic-regression analysis of linkage producing an empirical P=. 045 and with significant evidence for intersample heterogeneity (empirical P=.0096).

Allelic association studies provide the most powerful method for locating genes of small effect contributing to complex diseases and traits. However, in outbred populations, allelic association is usually maintained only over distances of <=1 cM. Therefore, systematic searches over large regions are costly. Here we present a method involving DNA pooling that can be used as a rapid preliminary screen for allelic association with the most common class of polymorphic markers, single-sequence repeats. Patient and control samples are pooled separately, and markers are typed in the two pools. By use of primers with fluorescent 5' ends, PCR products can be analyzed on an automated sequencing apparatus. Allele image patterns (AIPs) produced for the two groups are overlaid and differences in pattern area between pools computed. From this, a DeltaAIP statistic is calculated from the difference in areas between the two AIPs expressed as a fraction of the total shared and nonshared area. AIPs of a range of different-sized pools were generated by computer simulation for markers with a range of allele sizes and frequencies. DeltaAIPs from pools and chi2 values for individual genotypings were compared, with both simulated and real data from microsatellite markers. The results demonstrated a high correlation between DeltaAIP and chi2 values. DeltaAIP analysis of real microsatellite data indicated the feasibility of using this method in systematic searches for allelic association and generated a small number of false positives but few false negatives. We conclude that DeltaAIP analysis of DNA pools can be used effectively and efficiently as a rapid screen for allelic association in case-control studies.

Detection of linkage with a systematic genome scan in nuclear families including an affected sibling pair is an important initial step on the path to cloning susceptibility genes for complex genetic disorders, and it is desirable to optimize the efficiency of such studies. The aim is to maximize power while simultaneously minimizing the total number of genotypings and probability of type I error. One approach to increase efficiency, which has been investigated by other workers, is grid tightening: a sample is initially typed using a coarse grid of markers, and promising results are followed up by use of a finer grid. Another approach, not previously considered in detail in the context of an affected-sib-pair genome scan for linkage, is sample splitting: a portion of the sample is typed in the screening stage, and promising results are followed up in the whole sample. In the current study, we have used computer simulation to investigate the relative efficiency of two-stage strategies involving combinations of both grid tightening and sample splitting and found that the optimal strategy incorporates both approaches. In general, typing half the sample of affected pairs with a coarse grid of markers in the screening stage is an efficient strategy under a variety of conditions. If Hardy-Weinberg equilibrium holds, it is most efficient not to type parents in the screening stage. If Hardy-Weinberg equilibrium does not hold (e.g., because of stratification) failure to type parents in the first stage increases the amount of genotyping required, although the overall probability of type I error is not greatly increased, provided the parents are used in the final analysis.

In an affected-sib-pair study, the parents are often unavailable for typing, particularly for diseases of late onset. In many cases, however, it is possible to sample unaffected siblings. It is therefore desirable to assess the contribution of such siblings to the power of such a study. The likelihood ratio introduced by Risch and improved by Holmans was extended to incorporate data from unaffected siblings. Tests based on two likelihoods were considered: the full likelihood of the data, based on the identity-by-descent (IBD) sharing states of the entire sibship, and a pseudolikelihood based on the IBD sharing states of the affected pair only, using the unaffected siblings to infer parental genotypes. The latter approach was found to be more powerful, except when penetrance was high. Typing an unaffected sibling, or just one parent, was found to give only a small increase in power except when the PIC of the marker was low. Even then, typing an unaffected relative increased the overall number of individuals that had to be typed to achieve a given power. If there is no highly informative marker locus in the area under study, it may be possible to "build" one by combining the alleles from two or more neighboring tightly linked loci into haplotypes. Typing two loci gave a sizeable power increase over a single locus, but typing further loci gave much smaller gains. Building haplotypes will introduce phase uncertainties, with the result that such a system will yield less power than will a single locus with the same number of alleles. This power loss was small, however, and did not affect the conclusions regarding the worth of typing unaffected relatives.

The likelihood-ratio method for affected-sib-pair analysis, introduced by Risch, is a powerful method for detecting linkage when the marker is not perfectly polymorphic, as is often the case. The power of this method can be improved by restricting maximization to the set of possible haplotype-sharing probabilities--denoted the "possible triangle" method. The asymptotic distributions of the resulting distributions are derived, enabling test criteria to be found for any required test size (i.e., the probability of falsely detecting linkage when none exists) and enabling p values to be assigned to results. The criteria were found to be approximately constant when the PIC of the marker varies, making them applicable to any marker. The asymptotic power approximations were used to investigate the relative performance of pairs with typed parents, relative to those without, by comparing the sample sizes necessary for a given power. Under certain circumstances, typing the parents proved to be inefficient, even when PIC was low.