Faculty Opinions recommendation of Methods for association analysis and meta-analysis of rare variants in families.

AbstractHigh blood pressure (BP) is a major risk factor for many noncommunicable diseases. The effect of mitochondrial DNA single-nucleotide polymorphisms (mtSNPs) on BP is less known than that of nuclear SNPs. We investigated the mitochondrial genetic determinants of systolic, diastolic, and mean arterial BP. MtSNPs were determined from peripheral blood by sequencing or with genome-wide association study SNP arrays in two independent Finnish cohorts, the Young Finns Study and the Finnish Cardiovascular Study, respectively. In total, over 4200 individuals were included. The effects of individual common mtSNPs, with an additional focus on sex-specificity, and aggregates of rare mtSNPs grouped by mitochondrial genes were evaluated by meta-analysis of linear regression and a sequence kernel association test, respectively. We accounted for the predicted pathogenicity of the rare variants within protein-encoding and the tRNA regions. In the meta-analysis of 87 common mtSNPs, we did not observe significant associations with any of the BP traits. Sex-specific and rare-variant analyses did not pinpoint any significant associations either. Our results are in agreement with several previous studies suggesting that mtDNA variation does not have a significant role in the regulation of BP. Future studies might need to reconsider the mechanisms thought to link mtDNA with hypertension.

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Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2018-02496 ◽

2019 ◽

Vol 104 (9) ◽

pp. 3835-3850 ◽

Cited By ~ 13

Author(s):

Matthew Dapas ◽

Ryan Sisk ◽

Richard S Legro ◽

Margrit Urbanek ◽

Andrea Dunaif ◽

...

Keyword(s):

Polycystic Ovary Syndrome ◽

Quantitative Trait ◽

Rare Variants ◽

Polycystic Ovary ◽

Association Studies ◽

Meta Analysis ◽

Premenopausal Women ◽

Endocrine Disorders ◽

Genome Wide Association Studies ◽

Ovary Syndrome

AbstractContextPolycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5% to15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date.ObjectiveThe objective of this study was to test whether rare genetic variants contribute to PCOS pathogenesis.Design, Patients, and MethodsWe performed whole-genome sequencing on DNA from 261 individuals from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis.ResultsWe found rare variants in DENND1A (P = 5.31 × 10−5, adjusted P = 0.039) that were significantly associated with reproductive and metabolic traits in PCOS families.ConclusionsCommon variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.

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Collapsing Approaches for the Association Analysis of Rare Variants

Assessing Rare Variation in Complex Traits ◽

10.1007/978-1-4939-2824-8_10 ◽

2015 ◽

pp. 135-148

Author(s):

Jennifer L. Asimit ◽

Andrew Morris

Keyword(s):

Association Analysis ◽

Rare Variants

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Family-based quantitative trait meta-analysis implicates rare noncoding variants in DENND1A in pathogenesis of polycystic ovary syndrome

10.1101/460972 ◽

2018 ◽

Cited By ~ 1

Author(s):

Matthew Dapas ◽

Ryan Sisk ◽

Richard S. Legro ◽

Margrit Urbanek ◽

Andrea Dunaif ◽

...

Keyword(s):

Polycystic Ovary Syndrome ◽

Quantitative Trait ◽

Rare Variants ◽

Polycystic Ovary ◽

Association Studies ◽

Meta Analysis ◽

Premenopausal Women ◽

Endocrine Disorders ◽

Genome Wide Association Studies ◽

Ovary Syndrome

ABSTRACTPolycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5-15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. PCOS is a leading risk factor for type 2 diabetes in young women. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date. To test the hypothesis that rare genetic variants contribute to PCOS pathogenesis, we performed whole-genome sequencing on DNA from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis. We found rare variants in DENND1A (P=5.31×10−5, Padj=0.019) that were significantly associated with reproductive and metabolic traits in PCOS families. Common variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.

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Trans-ethnic meta-analysis of rare variants in sequencing association studies

Biostatistics ◽

10.1093/biostatistics/kxz061 ◽

2019 ◽

Author(s):

Jingchunzi Shi ◽

Michael Boehnke ◽

Seunggeun Lee

Keyword(s):

Rare Variants ◽

Sequence Data ◽

Association Studies ◽

Meta Analysis ◽

Error Rates ◽

Efficient Estimation ◽

Gene Region ◽

Type I ◽

Test Statistic ◽

Different Populations

Summary Trans-ethnic meta-analysis is a powerful tool for detecting novel loci in genetic association studies. However, in the presence of heterogeneity among different populations, existing gene-/region-based rare variants meta-analysis methods may be unsatisfactory because they do not consider genetic similarity or dissimilarity among different populations. In response, we propose a score test under the modified random effects model for gene-/region-based rare variants associations. We adapt the kernel regression framework to construct the model and incorporate genetic similarities across populations into modeling the heterogeneity structure of the genetic effect coefficients. We use a resampling-based copula method to approximate asymptotic distribution of the test statistic, enabling efficient estimation of p-values. Simulation studies show that our proposed method controls type I error rates and increases power over existing approaches in the presence of heterogeneity. We illustrate our method by analyzing T2D-GENES consortium exome sequence data to explore rare variant associations with several traits.

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