Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (P < 5.0 × 10) with PrCa and one locus significantly associated with early-onset PrCa (≤55 years). Our findings include missense variants rs1800057 (odds ratio (OR) = 1.16; P = 8.2 × 10; G>C, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa.

A recent genome-wide association study (GWAS) in African descent populations identified novel loci associated with skin pigmentation. However, how genomic variations affect skin pigmentation and how these skin pigmentation gene variants affect serum 25(OH) vitamin D variation has not been explored in African Americans (AAs). In order to further understand genetic factors that affect human skin pigmentation and serum 25(OH)D variation, we performed a GWAS for skin pigmentation with 395 AAs and a replication study with 681 AAs. Then, we tested if the identified variants are associated with serum 25(OH) D concentrations in a subset of AAs (n = 591). Skin pigmentation, Melanin Index (M-Index), was measured using a narrow-band reflectometer. Multiple regression analysis was performed to identify variants associated with M-Index and to assess their role in serum 25(OH)D variation adjusting for population stratification and relevant confounding variables. A variant near the SLC24A5 gene (rs2675345) showed the strongest signal of association with M-Index (P = 4.0 x 10−30 in the pooled dataset). Variants in SLC24A5, SLC45A2 and OCA2 together account for a large proportion of skin pigmentation variance (11%). The effects of these variants on M-Index was modified by sex (P for interaction = 0.009). However, West African Ancestry (WAA) also accounts for a large proportion of M-Index variance (23%). M-Index also varies among AAs with high WAA and high Genetic Score calculated from top variants associated with M-Index, suggesting that other unknown genomic factors related to WAA are likely contributing to skin pigmentation variation. M-Index was not associated with serum 25(OH)D concentrations, but the Genetic Score was significantly associated with vitamin D deficiency (serum 25(OH)D levels less than 12 ng/mL) (OR, 1.30; 95% CI, 1.04–1.64). The findings support the hypothesis suggesting that skin pigmentation evolved responding to increased demand for subcutaneous vitamin D synthesis in high latitude environments.

In the version of this article initially published, the name of author Manuela Gago-Dominguez was misspelled as Manuela Gago Dominguez. The error has been corrected in the HTML and PDF version of the article.

This paper presents a new and efficient method for simulating pseudo-genotype data using the standardized protocol of SLiM, which offers a flexible alternative to traditional methods that rely on large genetic datasets. These datasets can be time-consuming to obtain, especially when institutional review board (IRB) review is involved, making simulation an attractive alternative. While HapGen v2 is the most popular genotype simulator, we found that SLiM has the potential for more customizable simulation to meet multiple needs. To validate our new method, we compared its performance among parallel simulations varying multiple parameters. Our results showed that SLiM is capable of simulating samples up to 333 times the input size, with a low rate of simulated samples that are 2nd or closer relatives (REV), making it a promising alternative to HapGen. We also applied our whole-genome simulation approach to sensitivity analyses of chromosome-level principal component analysis (PCA) and kinship estimation. Our findings revealed important insights into the sensitivity of PCA and kinship estimation, highlighting the unequal distribution of population structure across chromosomes and ancestries. Furthermore, our study provides experimental support for avoiding chromosome-level quality control statistics. Overall, our standardized protocol of SLiM offers a flexible new way to produce pseudo-genotype data, and our findings provide valuable insights that can advance research in the field. By demonstrating the potential of SLiM for more customizable simulations and highlighting the importance of considering the distribution of population structure across chromosomes and ancestries, our research has significant implications for the study of genetics and genomics.