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Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions., (Copyright © 2020 American Society of Human Genetics. All rights reserved.)

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. Recently, genetic association studies that leverage external transcriptome data have received much attention and shown promise for discovering novel variants. One such approach, PrediXcan, is to use predicted gene expression through genetic regulation. However, there are limitations in this approach. The predicted gene expression may be biased, resulting from regularized regression applied to moderately sample-sized reference studies. Further, some variants can individually influence disease risk through alternative functional mechanisms besides expression. Thus, testing only the association of predicted gene expression as proposed in PrediXcan will potentially lose power. To tackle these challenges, we consider a unified mixed effects model that formulates the association of intermediate phenotypes such as imputed gene expression through fixed effects, while allowing residual effects of individual variants to be random. We consider a set-based score testing framework, MiST (mixed effects score test), and propose two data-driven combination approaches to jointly test for the fixed and random effects. We establish the asymptotic distributions, which enable rapid calculation of p values for genome-wide analyses, and provide p values for fixed and random effects separately to enhance interpretability over GWASs. Extensive simulations demonstrate that our approaches are more powerful than existing ones. We apply our approach to a large-scale GWAS of colorectal cancer and identify two genes, POU5F1B and ATF1, which would have otherwise been missed by PrediXcan, after adjusting for all known loci., (Copyright © 2018 American Society of Human Genetics. All rights reserved.)

Genome-wide association studies (GWASs) and functional genomics approaches implicate enhancer disruption in islet dysfunction and type 2 diabetes (T2D) risk. We applied genetic fine-mapping and functional (epi)genomic approaches to a T2D- and proinsulin-associated 15q22.2 locus to identify a most likely causal variant, determine its direction of effect, and elucidate plausible target genes. Fine-mapping and conditional analyses of proinsulin levels of 8,635 non-diabetic individuals from the METSIM study support a single association signal represented by a cluster of 16 strongly associated (p < 10 -17 ) variants in high linkage disequilibrium (r 2 > 0.8) with the GWAS index SNP rs7172432. These variants reside in an evolutionarily and functionally conserved islet and β cell stretch or super enhancer; the most strongly associated variant (rs7163757, p = 3 × 10 -19 ) overlaps a conserved islet open chromatin site. DNA sequence containing the rs7163757 risk allele displayed 2-fold higher enhancer activity than the non-risk allele in reporter assays (p < 0.01) and was differentially bound by β cell nuclear extract proteins. Transcription factor NFAT specifically potentiated risk-allele enhancer activity and altered patterns of nuclear protein binding to the risk allele in vitro, suggesting that it could be a factor mediating risk-allele effects. Finally, the rs7163757 proinsulin-raising and T2D risk allele (C) was associated with increased expression of C2CD4B, and possibly C2CD4A, both of which were induced by inflammatory cytokines, in human islets. Together, these data suggest that rs7163757 contributes to genetic risk of islet dysfunction and T2D by increasing NFAT-mediated islet enhancer activity and modulating C2CD4B, and possibly C2CD4A, expression in (patho)physiologic states., (Copyright © 2018 American Society of Human Genetics. All rights reserved.)

Autosomal-dominant tubulo-interstitial kidney disease (ADTKD) encompasses a group of disorders characterized by renal tubular and interstitial abnormalities, leading to slow progressive loss of kidney function requiring dialysis and kidney transplantation. Mutations in UMOD, MUC1, and REN are responsible for many, but not all, cases of ADTKD. We report on two families with ADTKD and congenital anemia accompanied by either intrauterine growth retardation or neutropenia. Ultrasound and kidney biopsy revealed small dysplastic kidneys with cysts and tubular atrophy with secondary glomerular sclerosis, respectively. Exclusion of known ADTKD genes coupled with linkage analysis, whole-exome sequencing, and targeted re-sequencing identified heterozygous missense variants in SEC61A1-c.553A>G (p.Thr185Ala) and c.200T>G (p.Val67Gly)-both affecting functionally important and conserved residues in SEC61. Both transiently expressed SEC6A1A variants are delocalized to the Golgi, a finding confirmed in a renal biopsy from an affected individual. Suppression or CRISPR-mediated deletions of sec61al2 in zebrafish embryos induced convolution defects of the pronephric tubules but not the pronephric ducts, consistent with the tubular atrophy observed in the affected individuals. Human mRNA encoding either of the two pathogenic alleles failed to rescue this phenotype as opposed to a complete rescue by human wild-type mRNA. Taken together, these findings provide a mechanism by which mutations in SEC61A1 lead to an autosomal-dominant syndromic form of progressive chronic kidney disease. We highlight protein translocation defects across the endoplasmic reticulum membrane, the principal role of the SEC61 complex, as a contributory pathogenic mechanism for ADTKD., (Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.)

Genome-wide association studies (GWASs) have identified more than 150 loci associated with blood lipid and cholesterol levels; however, the functional and molecular mechanisms for many associations are unknown. We examined the functional regulatory effects of candidate variants at the GALNT2 locus associated with high-density lipoprotein cholesterol (HDL-C). Fine-mapping and conditional analyses in the METSIM study identified a single locus harboring 25 noncoding variants (r(2) > 0.7 with the lead GWAS variants) strongly associated with total cholesterol in medium-sized HDL (e.g., rs17315646, p = 3.5 × 10(-12)). We used luciferase reporter assays in HepG2 cells to test all 25 variants for allelic differences in regulatory enhancer activity. rs2281721 showed allelic differences in transcriptional activity (75-fold [T] versus 27-fold [C] more than the empty-vector control), as did a separate 780-bp segment containing rs4846913, rs2144300, and rs6143660 (49-fold [AT(-) haplotype] versus 16-fold [CC(+) haplotype] more). Using electrophoretic mobility shift assays, we observed differential CEBPB binding to rs4846913, and we confirmed this binding in a native chromatin context by performing chromatin-immunoprecipitation (ChIP) assays in HepG2 and Huh-7 cell lines of differing genotypes. Additionally, sequence reads in HepG2 DNase-I-hypersensitivity and CEBPB ChIP-seq signals spanning rs4846913 showed significant allelic imbalance. Allelic-expression-imbalance assays performed with RNA from primary human hepatocyte samples and expression-quantitative-trait-locus (eQTL) data in human subcutaneous adipose tissue samples confirmed that alleles associated with increased HDL-C are associated with a modest increase in GALNT2 expression. Together, these data suggest that at least rs4846913 and rs2281721 play key roles in influencing GALNT2 expression at this HDL-C locus., (Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Genome-wide association studies (GWASs) have identified more than 70 loci associated with type 2 diabetes (T2D), but for most, the underlying causal variants, associated genes, and functional mechanisms remain unknown. At a T2D- and fasting-proinsulin-associated locus on 11q13.4, we have identified a functional regulatory DNA variant, a candidate target gene, and a plausible underlying molecular mechanism. Fine mapping, conditional analyses, and exome array genotyping in 8,635 individuals from the Metabolic Syndrome in Men study confirmed a single major association signal between fasting proinsulin and noncoding variants (p = 7.4 × 10(-50)). Measurement of allele-specific mRNA levels in human pancreatic islet samples heterozygous for rs11603334 showed that the T2D-risk and proinsulin-decreasing allele (C) is associated with increased ARAP1 expression (p < 0.02). We evaluated four candidate functional SNPs for allelic effects on transcriptional activity by performing reporter assays in rodent pancreatic beta cell lines. The C allele of rs11603334, located near one of the ARAP1 promoters, exhibited 2-fold higher transcriptional activity than did the T allele (p < 0.0001); three other candidate SNPs showed no allelic differences. Electrophoretic mobility shift assays demonstrated decreased binding of pancreatic beta cell transcriptional regulators PAX6 and PAX4 to the rs11603334 C allele. Collectively, these data suggest that the T2D-risk allele of rs11603334 could abrogate binding of a complex containing PAX6 and PAX4 and thus lead to increased promoter activity and ARAP1 expression in human pancreatic islets. This work suggests that increased ARAP1 expression might contribute to T2D susceptibility at this GWAS locus., (Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Age-related hearing impairment (ARHI), or presbycusis, is a very common multifactorial disorder. Despite the knowledge that genetics play an important role in the etiology of human ARHI as revealed by heritability studies, to date, its precise genetic determinants remain elusive. Here we report the results of a cross-sectional family-based genetic study employing audiometric data. By using principal component analysis, we were able to reduce the dimensionality of this multivariate phenotype while capturing most of the variation and retaining biologically important features of the audiograms. We conducted a genome-wide association as well as a linkage scan with high-density SNP microarrays. Because of the presence of genetic population substructure, association testing was stratified after which evidence was combined by meta-analysis. No association signals reaching genome-wide significance were detected. Linkage analysis identified a linkage peak on 8q24.13-q24.22 for a trait correlated to audiogram shape. The signal reached genome-wide significance, as assessed by simulations. This finding represents the first locus for an ARHI trait.