Controlling for human population stratification in rare variant association studies

Comprehensive Molecular Analysis of Disease-Related Genes as First-Tier Test for Early Diagnosis, Classification, and Management of Patients Affected by Nonsyndromic Ichthyosis

Inherited ichthyoses are a group of clinically and genetically heterogeneous rare disorders of skin keratinization with overlapping phenotypes. The clinical picture and family history are crucial to formulating the diagnostic hypothesis, but only the identification of the genetic defect allows the correct classification. In the attempt to molecularly classify 17 unrelated Italian patients referred with congenital nonsyndromic ichthyosis, we performed massively parallel sequencing of over 50 ichthyosis-related genes. Genetic data of 300 Italian unaffected subjects were also analyzed to evaluate frequencies of putative disease-causing alleles in our population. For all patients, we identified the molecular cause of the disease. Eight patients were affected by autosomal recessive congenital ichthyosis associated with ALOX12B, NIPAL4, and TGM1 mutations. Three patients had biallelic loss-of-function variants in FLG, whereas 6/11 males were affected by X-linked ichthyosis. Among the 24 different disease-causing alleles we identified, 8 carried novel variants, including a synonymous TGM1 variant that resulted in a splicing defect. Moreover, we generated a priority list of the ichthyosis-related genes that showed a significant number of rare and novel variants in our population. In conclusion, our comprehensive molecular analysis resulted in an effective first-tier test for the early classification of ichthyosis patients. It also expands the genetic, mutational, and phenotypic spectra of inherited ichthyosis and provides new insight into the current understanding of etiologies and epidemiology of this group of rare disorders.

Genetic architecture and biology of youth-onset type 2 diabetes

The prevalence of youth-onset type 2 diabetes (T2D) and childhood obesity has been rising steadily1, producing a growing public health concern1 that disproportionately affects minority groups2. The genetic basis of youth-onset T2D and its relationship to other forms of diabetes are unclear3. Here we report a detailed genetic characterization of youth-onset T2D by analysing exome sequences and common variant associations for 3,005 individuals with youth-onset T2D and 9,777 adult control participants matched for ancestry, including both males and females. We identify monogenic diabetes variants in 2.4% of individuals and three exome-wide significant (P < 2.6 × 10-6) gene-level associations (HNF1A, MC4R, ATXN2L). Furthermore, we report rare variant association enrichments within 25 gene sets related to obesity, monogenic diabetes and β-cell function. Many youth-onset T2D associations are shared with adult-onset T2D, but genetic risk factors of all frequencies-and rare variants in particular-are enriched within youth-onset T2D cases (5.0-fold increase in the rare variant and 3.4-fold increase in common variant genetic liability relative to adult-onset cases). The clinical presentation of participants with youth-onset T2D is influenced in part by the frequency of genetic risk factors within each individual. These findings portray youth-onset T2D as a heterogeneous disease situated on a spectrum between monogenic diabetes and adult-onset T2D.

Severe adult hemophagocytic lymphohistiocytosis (HLHa) correlates with HLH-related gene variants

BACKGROUND

The contribution of genetic factors to the severity of adult hemophagocytic lymphohistiocytosis (HLHa) remains unclear.

OBJECTIVE

We sought to assess a potential link between HLHa outcomes and HLH-related gene variants.

METHODS

Clinical characteristics of 130 HLHa patients (age ≥ 18 years and HScore ≥ 169) and genotype of 8 HLH-related genes (LYST, PRF1, UNC13-D, STX11, STXBP2, RAB27A, XIAP, and SAP) were collected. A total of 34 variants found in only 6 genes were selected on the basis of their frequency and criteria predicted to impair protein function. Severity was defined by refractory disease to HLH treatment, death, or transfer to an intensive care unit.

RESULTS

HLHa-associated diseases (ADs) were neoplasia (n = 49 [37.7%]), autoimmune/inflammatory disease (n = 33 [25.4%]), or idiopathic when no AD was identified (n = 48 [36.9%]). Infectious events occurred in 76 (58.5%) patients and were equally distributed in all ADs. Severe and refractory HLHa were observed in 80 (61.5%) and 64 (49.2%) patients, respectively. HScore, age, sex ratio, AD, and infectious events showed no significant association with HLHa severity. Variants were identified in 71 alleles and were present in 56 (43.1%) patients. They were distributed as follows: 44 (34.4%), 9 (6.9%), and 3 (2.3%) patients carrying 1, 2, and 3 variant alleles, respectively. In a logistic regression model, only the number of variants was significantly associated with HLHa severity (1 vs 0: 3.86 [1.73-9.14], P = .0008; 2-3 vs 0: 29.4 [3.62-3810], P = .0002) and refractoriness (1 vs 0: 2.47 [1.17-5.34], P = .018; 2-3 vs 0: 13.2 [2.91-126.8], P = .0003).

CONCLUSIONS

HLH-related gene variants may be key components to the severity and refractoriness of HLHa.

Insights from rare variants into the genetic architecture and biology of youth-onset type 2 diabetes

Youth-onset type 2 diabetes (T2D) is a growing public health concern. Its genetic basis and relationship to other forms of diabetes are largely unknown. To gain insight into the genetic architecture and biology of youth-onset T2D, we analyzed exome sequences of 3,005 youth-onset T2D cases and 9,777 ancestry matched adult controls. We identified (a) monogenic diabetes variants in 2.1% of individuals; (b) two exome-wide significant (P < 4.3×10-7) common coding variant associations (in WFS1 and SLC30A8); (c) three exome-wide significant (P < 2.5×10-6) rare variant gene-level associations (HNF1A, MC4R, ATX2NL); and (d) rare variant association enrichments within 25 gene sets broadly related to obesity, monogenic diabetes, and β-cell function. Many association signals were shared between youth-onset and adult-onset T2D but had larger effects for youth-onset T2D risk (1.18-fold increase for common variants and 2.86-fold increase for rare variants). Both common and rare variant associations contributed more to youth-onset T2D liability variance than they did to adult-onset T2D, but the relative increase was larger for rare variant associations (5.0-fold) than for common variant associations (3.4-fold). Youth-onset T2D cases showed phenotypic differences depending on whether their genetic risk was driven by common variants (primarily related to insulin resistance) or rare variants (primarily related to β-cell dysfunction). These data paint a picture of youth-onset T2D as a disease genetically similar to both monogenic diabetes and adult-onset T2D, in which genetic heterogeneity might be used to sub-classify patients for different treatment strategies.

An exploration of linkage fine-mapping on sequences from case-control studies

Linkage analysis maps genetic loci for a heritable trait by identifying genomic regions with excess relatedness among individuals with similar trait values. Analysis may be conducted on related individuals from families, or on samples of unrelated individuals from a population. For allelically heterogeneous traits, population-based linkage analysis can be more powerful than genotypic-association analysis. Here, we focus on linkage analysis in a population sample, but use sequences rather than individuals as our unit of observation. Earlier investigations of sequence-based linkage mapping relied on known sequence relatedness, whereas we infer relatedness from the sequence data. We propose two ways to associate similarity in relatedness of sequences with similarity in their trait values and compare the resulting linkage methods to two genotypic-association methods. We also introduce a procedure to label case sequences as potential carriers or noncarriers of causal variants after an association has been found. This post hoc labeling of case sequences is based on inferred relatedness to other case sequences. Our simulation results indicate that methods based on sequence relatedness improve localization and perform as well as genotypic-association methods for detecting rare causal variants. Sequence-based linkage analysis therefore has potential to fine-map allelically heterogeneous disease traits.

Rare genetic variants explain missing heritability in smoking

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Recent advances and challenges of rare variant association analysis in the biobank sequencing era

Causal variants for rare genetic diseases are often rare in the general population. Rare variants may also contribute to common complex traits and can have much larger per-allele effect sizes than common variants, although power to detect these associations can be limited. Sequencing costs have steadily declined with technological advancements, making it feasible to adopt whole-exome and whole-genome profiling for large biobank-scale sample sizes. These large amounts of sequencing data provide both opportunities and challenges for rare-variant association analysis. Herein, we review the basic concepts of rare-variant analysis methods, the current state-of-the-art methods in utilizing variant annotations or external controls to improve the statistical power, and particular challenges facing rare variant analysis such as accounting for population structure, extremely unbalanced case-control design. We also review recent advances and challenges in rare variant analysis for familial sequencing data and for more complex phenotypes such as survival data. Finally, we discuss other potential directions for further methodology investigation.

Clarifying the causes of consistent and inconsistent findings in genetics

As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family‐ and genome‐wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.