Height-reducing variants and selection for short stature in Sardinia

Żołędziewska, Magdalena; Sidore, Carlo; Chiang, Charleston W.K.; Sanna, Serena; Mulas, Antonella; Steri, Maristella; Busonero, Fabio; Marcus, Joseph; Marongiu, Michele; Maschio, Andrea; Vecchyo, Diego Ortega Del; Floris, Matteo; Meloni, Antonella; Delitala, Alessandro; Concas, Maria Pina; Murgia, Federico; Biino, Ginevra; Vaccargiu, Simona; Nagaraja, Ramaiah; Lohmueller, Kirk E.; Timpson, Nicholas J.; Soranzo, Nicole; Tachmazidou, Ioanna; Dedoussis, George; Zeggini, Eleftheria; Uzzau, Sergio; Jones, Chris; Lyons, Robert H.; Angius, Andrea; Abecasis, Gonçalo R.; Novembre, John; Schlessinger, David; Cucca, Francesco

doi:10.1038/ng.3403

Cited by 100 publications

(106 citation statements)

References 36 publications

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“…Since previous ACAN Furthermore, none of our patients carried apparently pathogenic mutations in the genes of the GH-IGF1 axis. Although we detected rare damaging variants in GHRHR, GHR, and IGFALS, the etiological relationship between these variants and the patients' phenotype remained unclear, because heterozygous mutations in GHRHR usually permit normal growth [24][25][26][27] and those in GHR and IGFALS are known to exert only mild effects on growth [28,29]. For example, previous cases with heterozygous IGFALS mutations had final heights of around −1.0 SD [29].…”

Section: Discussionmentioning

confidence: 99%

Next generation sequencing-based mutation screening of 86 patients with idiopathic short stature

et al. 2017

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Abstract. Although mutations in ACAN, FGFR3, NPR2, and SHOX typically lead to skeletal dysplasia, and mutations in GHRHR, GH1, GHR, STAT5B, IGF1, IGFALS, and IGF1R usually underlie hormonal defects of the growth hormone (GH)-insulin-like growth factor 1 (IGF1) axis, such mutations have also been identified in patients with idiopathic short stature (ISS). Of these, SHOX abnormalities are known to account for a certain percentage of ISS cases, whereas the frequency of mutations in the other 10 genes in ISS cohorts remains unknown. Here, we performed next-generation sequencing-based mutation screening of the 10 genes in 86 unrelated Japanese ISS patients without SHOX abnormalities. We searched for rare protein-altering variants. The functional significance of the identified variants was assessed by in silico analyses. Consequently, we identified 18 heterozygous rare variants in 19 patients, including four probable damaging variants in ACAN, six pathogenicity-unknown variants in FGFR3, GHRHR, GHR, and IGFALS, and eight possible benign variants. Pathogenic variants in NPR2, GH1, and IGF1 were absent from our cohort. Unlike previously reported patients with ACAN mutations, our four patients with ACAN variants manifested non-specific short stature with age-appropriate or mildly delayed bone ages, and had parents of normal stature. These results indicate that ACAN mutations can underlie ISS without characteristic skeletal features, and that such mutations are possibly associated with de novo occurrence or low penetrance. In addition, our data imply that mutations in FGFR3, NPR2, and GH-IGF1 axis genes play only limited roles in the etiology of ISS.

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Section: Discussionmentioning

confidence: 99%

Next generation sequencing-based mutation screening of 86 patients with idiopathic short stature

et al. 2017

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“…We note that in addition to population stratification, polygenic selection, which is thought to be acting on height in Europe [30][31][32] , could induce correlation between PC loadings and summary statistics. This correlation could in principle be incorrectly interpreted as population stratification and cause PC loading regression to correct away true signals.…”

Section: Discussionmentioning

confidence: 99%

Correcting subtle stratification in summary association statistics

Bhatia

Furlotte

Loh

et al. 2016

Preprint

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Population stratification is a well-documented confounder in GWASes, and is often addressed by including principal component (PC) covariates computed from common SNPs (SNP-PCs). In our analyses of summary statistics from 36 GWASes (mean n=88k), including 20 GWASes using 23andMe data that included SNP-PC covariates, we observed a significantly inflated LD score regression (LDSC) intercept for several traits-suggesting that residual stratification remains a concern, even when SNP-PC covariates are included.Here we propose a new method, PC loading regression, to correct for stratification in summary statistics by leveraging SNP loadings for PCs computed in a large reference panel. In addition to SNP-PCs, the method can be applied to haploSNP-PCs, i.e. PCs computed from a larger number of rare haplotype variants that better capture subtle structure. Using simulations based on real genotypes from 54,000 individuals of diverse European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, we show that PC loading regression effectively corrects for stratification along top PCs.We applied PC loading regression to several traits with inflated LDSC intercepts. Correcting for the top four SNP-PCs in GERA data, we observe a significant reduction in LDSC intercept height summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium, but not for 23andMe summary statistics, which already included SNP-PC covariates. However, when correcting for additional haploSNP-PCs in 23andMe GWASes, inflation in the LDSC intercept was eliminated for eye color, hair color, and skin color and substantially reduced for height (1.41 to 1.16; n=430k). Correcting for haploSNP-PCs in GIANT height summary statistics eliminated inflation in the LDSC intercept (from 1.35 to 1.00; n=250k), eliminating 27 significant association signals including one at the LCT locus, which is highly differentiated among European populations and widely known to produce spurious signals. Overall, our results suggest that uncorrected population stratification is a concern in GWASes of large sample size and that PC loading regression can correct for this stratification.

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“…polygenic models). Signals of polygenic selection can be identified by various methods, such as correlation of allele frequencies [1][2][3][4] and the regression of average trait values on polygenic scores (PS) [2,[5][6][7], which have been successfully applied to human stature [5][6][7] and cognitive abilities [2]. This paper has several aims: to test the presence of correlated frequencies among GWAS hits and the predictive power of polygenic scores (average frequencies of GWAS alleles with positive effect), independently of spatial autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

<strong><b>Can We Detect Polygenic Selection on Cognitive Ability Using GWAS Hits? Employing Random SNPs as a Null Model.</b></strong>

Piffer

2017

Preprint

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Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment were used to test a polygenic selection model. ethods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. A null model was created using frequencies of random SNPs. Results: Polygenic selection signal of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications. A polygenic score comprising 9 SNPs predicts population IQ (r=0.9), outperforming 99.9% of the polygenic scores obtained from sets of random SNPs. Its predictive power remains unaffected after controlling for spatial autocorrelation. Even random polygenic scores are moderate predictors of population IQ (thanks to spatial autocorrelation), and their predictive power increases logarithmically with the number of SNPs, indicating an exponential reduction in noise. Conclusion: This study provides guidance for using GWAS hits together with random SNPs for testing polygenic selection.

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Height-reducing variants and selection for short stature in Sardinia

Cited by 100 publications

References 36 publications

Next generation sequencing-based mutation screening of 86 patients with idiopathic short stature

Next generation sequencing-based mutation screening of 86 patients with idiopathic short stature

Correcting subtle stratification in summary association statistics

<strong><b>Can We Detect Polygenic Selection on Cognitive Ability Using GWAS Hits? Employing Random SNPs as a Null Model.</b></strong>

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